{"id":4359,"date":"2026-05-18T05:21:24","date_gmt":"2026-05-18T05:21:24","guid":{"rendered":"https:\/\/proleed.academy\/blog\/?p=4359"},"modified":"2026-05-25T04:41:33","modified_gmt":"2026-05-25T04:41:33","slug":"context-windows-explained-why-long-context-llms-still-forget-information","status":"publish","type":"post","link":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/","title":{"rendered":"Context Windows Explained: Why Long- Context LLM&#8217;s Still Forget Information"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"4359\" class=\"elementor elementor-4359\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2373652 elementor-section-full_width elementor-section-height-default elementor-section-height-default\" data-id=\"2373652\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-c8327d9\" data-id=\"c8327d9\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-85ac09c elementor-widget elementor-widget-heading\" data-id=\"85ac09c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Table of contents<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6a368f5 elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"6a368f5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#topic1\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-chevron-right\"><\/i>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">What is a context window in an LLM?<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#topic2\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-chevron-right\"><\/i>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">How long-context LLMs work: 8K vs. 128K vs. 1M tokens<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#topic3\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-chevron-right\"><\/i>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Why LLMs still forget \u2014 the serial position effect<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#topic4\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-chevron-right\"><\/i>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Context window vs. long-term memory: what is the difference?<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#topic5\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-chevron-right\"><\/i>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">The \"needle in a haystack\" test: measuring real retrieval accuracy<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#topic6\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-chevron-right\"><\/i>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">5 practical strategies to get better results from long-context models<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#topic7\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-chevron-right\"><\/i>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"> Is a bigger context window always better?<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#topic8\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-chevron-right\"><\/i>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Key takeaways and what to watch in 2026 and beyond<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#topic9\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-chevron-right\"><\/i>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">FAQ \u2014 Common Questions<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-add9961\" data-id=\"add9961\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1e33d83 elementor-widget elementor-widget-heading\" data-id=\"1e33d83\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Context Windows Explained:\nWhy Long-Context LLMs Still Forget Information\n<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c86692a elementor-widget elementor-widget-image\" data-id=\"c86692a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"960\" height=\"640\" src=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-long-context-llms-still-forget-information.webp\" class=\"attachment-large size-large wp-image-4361\" alt=\"why-long-context-llms-still-forget-information\" srcset=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-long-context-llms-still-forget-information.webp 960w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-long-context-llms-still-forget-information-300x200.webp 300w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-long-context-llms-still-forget-information-768x512.webp 768w\" sizes=\"(max-width: 960px) 100vw, 960px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d2c912b elementor-widget elementor-widget-text-editor\" data-id=\"d2c912b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Many AI users have had a moment where they complete several complex instructions in a chatbot (by copy\/pasting), and then add a large text document with a question at the end asking for a response. The model will generate a response as if it did not read half of what you sent; it creates confusion. The model does appear to have processed the text and at some point in the conversation it does correctly reference some of the things in your document, yet it seems that something important has been missed along the way.<\/p><p>To find out why you see that happen, we will need to discuss one of the most fundamental concepts in AI (however often misunderstood) \u2014 the context window, to truly understand it you will need to have a basic level of understanding relating to other concepts (i.e.: tokenization, attention, memory architecture, etc.). If you are just starting your AI learning, <span style=\"text-decoration: underline;\"><strong><em><a href=\"https:\/\/proleed.academy\/ai-and-machine-learning-training-course.php\">AI Course<\/a><\/em><\/strong><\/span> provides an excellent place to begin as it is broken down in such a way (from tokens to transformers) that every other part of this article will make sense to you much more quickly.\u00a0 Now we will elaborate further into this.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ea44d87 elementor-widget elementor-widget-menu-anchor\" data-id=\"ea44d87\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"topic1\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bce85f5 elementor-widget elementor-widget-heading\" data-id=\"bce85f5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What is a context window in an LLM?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8969558 elementor-widget elementor-widget-image\" data-id=\"8969558\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/what-an-ai-model-actually-see-1024x683.webp\" class=\"attachment-large size-large wp-image-4366\" alt=\"what-an-ai-model-actually-see\" srcset=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/what-an-ai-model-actually-see-1024x683.webp 1024w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/what-an-ai-model-actually-see-300x200.webp 300w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/what-an-ai-model-actually-see-768x512.webp 768w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/what-an-ai-model-actually-see.webp 1379w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8588eb7 elementor-widget elementor-widget-text-editor\" data-id=\"8588eb7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The context window of a language model is the total amount of text the model has access to and can process at one time. It\u2019s like a working desk &#8211; everything on the desk can be viewed and accessed by the model; anything in a drawer or outside of the desk does not exist for the model at that time.<br \/>The context is not measured as words\/characters, though; it is measured in tokens instead. A token is equal to an English word on average, although this varies with different languages and types of content. Consequently, the word &#8220;unbelievable&#8221; could be broken up into 2-3 tokens; whereas a single line of code written in Python could have 4 tokens associated with it. It is important to understand how many begin and end points exist with what you can expect from a model when saying that the model has a context window of 128k will allow for 128,000 tokens.<\/p><table width=\"624\"><tbody><tr><td width=\"13\"><p>\u00a0<\/p><\/td><td width=\"611\"><p><strong>IMPORTANT DISTINCTION<\/strong><\/p><p>The context window is NOT a form of memory. It is stateless and temporary and is completely reset whenever a session ends. Unless you re-insert prior text into the current session, the model will not recall anything from the previous day\u2019s conversation.<\/p><\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0decc61 elementor-widget elementor-widget-menu-anchor\" data-id=\"0decc61\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"topic2\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-74901de elementor-widget elementor-widget-heading\" data-id=\"74901de\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">How long-context LLMs work: 8K vs. 128K vs. 1M tokens<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-504ddb7 elementor-widget elementor-widget-image\" data-id=\"504ddb7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1379\" height=\"757\" src=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/the-rapid-evolution-of-ai-context-windows-1.webp\" class=\"attachment-full size-full wp-image-4371\" alt=\"the-rapid-evolution-of-ai-context-windows\" srcset=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/the-rapid-evolution-of-ai-context-windows-1.webp 1379w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/the-rapid-evolution-of-ai-context-windows-1-300x165.webp 300w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/the-rapid-evolution-of-ai-context-windows-1-1024x562.webp 1024w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/the-rapid-evolution-of-ai-context-windows-1-768x422.webp 768w\" sizes=\"(max-width: 1379px) 100vw, 1379px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86d805b elementor-widget elementor-widget-text-editor\" data-id=\"86d805b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The size of context windows has changed greatly in a short time span. The original GPT-3 had a limit of 4,096 tokens \u2014 which is enough for several pages of text to be included. In 2023, models typically had context windows of 32K and 128K tokens, whereas Google\u2019s Gemini 1.5 Pro had expanded to a million by 2024 and into 2025.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-12ca9a2 elementor-widget elementor-widget-text-editor\" data-id=\"12ca9a2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"customTable\">\n<table>\n<tbody>\n<tr>\n<td width=\"160\"><strong>Context Size<\/strong><\/td>\n<td width=\"192\"><strong>Approximate Word<\/strong><\/td>\n<td width=\"272\"><strong>Equivalent Real-Life Example<\/strong><\/td>\n<\/tr>\n<tr>\n<td width=\"160\">8,000 tokens<\/td>\n<td width=\"192\">~6,000 words<\/td>\n<td width=\"272\">Long magazine-style article<\/td>\n<\/tr>\n<tr>\n<td width=\"160\">32,000 tokens<\/td>\n<td width=\"192\">~24,000 words<\/td>\n<td width=\"272\">Short novella<\/td>\n<\/tr>\n<tr>\n<td width=\"160\">128,000 tokens<\/td>\n<td width=\"192\">~96,000 words<\/td>\n<td width=\"272\">Standard length novel<\/td>\n<\/tr>\n<tr>\n<td width=\"160\">1 million tokens<\/td>\n<td width=\"192\">~750,000 words<\/td>\n<td width=\"272\">Full codebase or 8 novels combined<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8e3933d elementor-widget elementor-widget-text-editor\" data-id=\"8e3933d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>By the time you fill a million-digit window, it is not just a slow process but an expensive one as well. The second major factor affecting costs not widely known is that of the Key Value (KV) cache used in many contexts. A kv cache allows for improved performance by only having to calculate the computation on tokens that have not already been cached. The costs associated with filling an entire million-token window will be somewhere between #1 and 10 times greater than filling an 8k token window. This is why filling a hundred-million token window with data will never make it unnecessary to fill shorter data windows when performing searches for targeted results.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a62febb elementor-widget elementor-widget-menu-anchor\" data-id=\"a62febb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"topic3\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c33957e elementor-widget elementor-widget-heading\" data-id=\"c33957e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Why LLMs still forget \u2014 the serial position effect<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e99d1c elementor-widget elementor-widget-image\" data-id=\"3e99d1c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1379\" height=\"920\" src=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-ai-forgets-information-in-the-middle.webp\" class=\"attachment-full size-full wp-image-4373\" alt=\"why-ai-forgets-information-in-the-middle\" srcset=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-ai-forgets-information-in-the-middle.webp 1379w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-ai-forgets-information-in-the-middle-300x200.webp 300w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-ai-forgets-information-in-the-middle-1024x683.webp 1024w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-ai-forgets-information-in-the-middle-768x512.webp 768w\" sizes=\"(max-width: 1379px) 100vw, 1379px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7ca6802 elementor-widget elementor-widget-text-editor\" data-id=\"7ca6802\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>There\u2019s a paradox with transformer models. A model can \u201csee\u201d everything in its context window (or memory), but it generally underperforms on things within the middle of that window\u2014this isn\u2019t a bug; it\u2019s due to the way attention works and is called the \u201c<strong>serial position effect<\/strong>\u201d in cognitive psychology.<\/p><p>\u00a0<\/p><table width=\"624\"><tbody><tr><td width=\"12\"><p>\u00a0<\/p><\/td><td width=\"612\"><p><em>&#8220;Models don&#8217;t forget uniformly. They forget in a U-shape \u2014 strong recall at the start, strong recall at the end, and a significant dip in the middle.&#8221;<\/em><\/p><p>\u2014 Lost in the Middle, Stanford NLP Research, 2023<\/p><\/td><\/tr><\/tbody><\/table><p>\u00a0<\/p><p>The serial position effect arises from attention dilution because while all tokens in a transformer model attend to all other tokens, the strength of attention is not the same. Positional encoding makes tokens at the start of a context (system prompts, main instructions, etc.) get way more attention than they normally would. Similarly, tokens at the end will also receive more attention than average due to recency (last used when generating the model).<\/p><p>However, tokens in the middle get less attention than normal\u2014especially if you have a long document\u2014and, therefore, the model will produce inferior answers if there is a question whose answer is located halfway through a 50-page PDF than if the answer was at page 1 or page 50.<\/p><p>\u00a0<\/p><table width=\"624\"><tbody><tr><td width=\"13\"><p>\u00a0<\/p><\/td><td width=\"611\"><p><strong>EXAMPLE OF REAL FAILURE<\/strong><\/p><p>\u00a0<\/p><p>A developer submits a system prompt (200 lines) followed by a technology reference document (10,000 words) and a statement to the effect of &#8220;Follow my formatting guidelines.&#8221; The model produces output with complete confidence but completely disregards the formatting guidelines as they were located in a high-dilution middle point in the entire context of the input.<\/p><\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0f53b24 elementor-widget elementor-widget-menu-anchor\" data-id=\"0f53b24\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"topic4\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d4c3fea elementor-widget elementor-widget-heading\" data-id=\"d4c3fea\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Context window vs. long-term memory: what is the difference?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2e19eaa elementor-widget elementor-widget-image\" data-id=\"2e19eaa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/temporary-context-vs-real-memory-1024x683.webp\" class=\"attachment-large size-large wp-image-4374\" alt=\"temporary-context-vs-real-memory\" srcset=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/temporary-context-vs-real-memory-1024x683.webp 1024w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/temporary-context-vs-real-memory-300x200.webp 300w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/temporary-context-vs-real-memory-768x512.webp 768w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/temporary-context-vs-real-memory.webp 1379w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2f3325a elementor-widget elementor-widget-text-editor\" data-id=\"2f3325a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The most frequently searched and therefore confused distinction in AI is context window versus long-term memory systems. Context windows are temporary, and once a session is closed, all context from that session disappears, so there is no permanent recollection of any user&#8217;s name, preferences, or what they sent in last week&#8217;s message unless that information is re-injected into a new context.<\/p><p>Long-term memory systems are built on top of LLMs using an external database, vector stores, or RAG to allow for storing information out of the model and retrieving relevant information on an as-needed basis. Therefore, it is common for people to ask whether the existence of large context windows renders RAG irrelevant.<\/p><div class=\"customTable\"><table><tbody><tr><td><p><strong>\u2715<\/strong><strong>\u00a0 THE MYTH<\/strong><\/p><p>A 1M token context window means we could just dump our whole knowledge base as a prompt, bypassing RAG entirely.<\/p><\/td><td width=\"312\"><p><strong>\u2713<\/strong><strong>\u00a0 THE REALITY<\/strong><\/p><p>Due to cost, latency, and the serial position effect, this is not feasible at scale. RAG will only return what is relevant for you to query with. As such, you&#8217;ll have lower cost, level of precision would be greater, and you won&#8217;t be able to query across many areas of content.<\/p><\/td><\/tr><\/tbody><\/table><\/div><p>The answer is an unequivocal no. Large context windows do not render RAG redundant; rather, they complement each other. For one-time analyses of documents on occasion, a large context window is an appropriate option; however, if thousands\/millions of repeated queries will be undertaken against many documents, then RAG is much more economical than large context windows and provides higher-quality results since RAG avoids drowning relevant content among an overwhelming number of irrelevant tokens.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5d5dc7f elementor-widget elementor-widget-menu-anchor\" data-id=\"5d5dc7f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"topic5\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0d39015 elementor-widget elementor-widget-heading\" data-id=\"0d39015\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The \"needle in a haystack\" test: measuring real retrieval accuracy<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-298cbaf elementor-widget elementor-widget-image\" data-id=\"298cbaf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/the-needle-in-haystack-problem-1024x683.webp\" class=\"attachment-large size-large wp-image-4375\" alt=\"the-needle-in-haystack-problem\" srcset=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/the-needle-in-haystack-problem-1024x683.webp 1024w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/the-needle-in-haystack-problem-300x200.webp 300w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/the-needle-in-haystack-problem-768x512.webp 768w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/the-needle-in-haystack-problem.webp 1379w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4f654a6 elementor-widget elementor-widget-text-editor\" data-id=\"4f654a6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li><p>The most popular benchmark used for testing long context recall performance is the &#8220;needle in a haystack&#8221; test. The test is quite simple: the needle is a fact or a piece of information that you want to find. The haystack is a long, filler, block of text within which the needle can be found at a specific location. The model used to test the long term memory of the needle is the same model that will be tested multiple times in different context sizes and\/or positions.<\/p><p>The findings from the testing illustrate a very valuable piece of information &#8212; almost every model tested performed nearly perfectly when the needle was located near either the beginning or the end of the haystack, however the model performance dropped significantly (sometimes quite severely) when the needle was located in the middle (30-70% of the haystack length). Furthermore, models that are advertised as performing perfectly within their context windows have been independently tested and found to exhibit the aforementioned middle location deficiencies.<\/p><p>\u00a0<\/p><table width=\"624\"><tbody><tr><td width=\"13\"><p>\u00a0<\/p><\/td><td width=\"611\"><p><strong>WHAT DOES THIS MEAN TO YOU<\/strong><\/p><p>You should never be under the impression that a model has the ability to keep and remember everything provided to it. If you are going to give the model critical data, provide the data at the beginning of a prompt versus somewhere in the middle of a large document.<\/p><\/td><\/tr><\/tbody><\/table><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-986da51 elementor-widget elementor-widget-menu-anchor\" data-id=\"986da51\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"topic6\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d1b368e elementor-widget elementor-widget-heading\" data-id=\"d1b368e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">5 practical strategies to get better results from long-context models<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4e82bde elementor-widget elementor-widget-image\" data-id=\"4e82bde\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1379\" height=\"920\" src=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/5-ways-to-get-better-results-from-ai.webp\" class=\"attachment-full size-full wp-image-4376\" alt=\"\" srcset=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/5-ways-to-get-better-results-from-ai.webp 1379w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/5-ways-to-get-better-results-from-ai-300x200.webp 300w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/5-ways-to-get-better-results-from-ai-1024x683.webp 1024w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/5-ways-to-get-better-results-from-ai-768x512.webp 768w\" sizes=\"(max-width: 1379px) 100vw, 1379px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-62e553f elementor-widget elementor-widget-text-editor\" data-id=\"62e553f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Knowing how to use the theoretical body of knowledge is important. Having practical tips on how to apply the theory will give you the information you need to create high-quality output with respect to large contexts. Below are five evidence-based strategies that result in improved quality of output from large contexts:<\/p><p><strong>1 <\/strong><strong>Lead with the most important information.<br \/><\/strong>The first thing in a prompt, i.e. the first thing before any other information, should be key instructions, constraints, and the most important factual information. The first part of the context will receive the most attention by the system.<\/p><p><strong>2 <\/strong><strong>Summarize the document prior to asking the question.<br \/><\/strong>If you are going to provide the system with the context of a long document before asking it a question, provide it with a summary of the document before giving it the complete document. This increases the signal to noise ratio and therefore eliminates dilution.<\/p><p><strong>3 <\/strong><strong>If you are going to use the same knowledge base repeatedly, consider using RAG.<br \/><\/strong>Retrieval Augmented Generation provides an integrated means of completing an output in a timely manner and at lower cost when data is large and repetitive as compared to using a large number of individual data sets.<\/p><p><strong>4 <\/strong><strong>Split up &amp; ask questions, don&#8217;t put everything in one big file.<br \/><\/strong>Instead of copying the entire document 200 pages long, break it down into smaller parts and only ask for those parts of interest. Smaller focused areas will provide better results than larger areas on specific retrieval objectives.<\/p><p><strong>5 <\/strong><strong>Repeat the most important instruction at the end.<br \/><\/strong>Because the recency effect is in your favour, a final reminder of one critical instruction (especially for formatting and style) will increase your chances of successful compliance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8a0769b elementor-widget elementor-widget-menu-anchor\" data-id=\"8a0769b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"topic7\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e72714 elementor-widget elementor-widget-heading\" data-id=\"3e72714\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Is a bigger context window always better?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-38d4210 elementor-widget elementor-widget-image\" data-id=\"38d4210\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-bigger-context-windows-are-not-always-better-1024x683.webp\" class=\"attachment-large size-large wp-image-4377\" alt=\"\" srcset=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-bigger-context-windows-are-not-always-better-1024x683.webp 1024w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-bigger-context-windows-are-not-always-better-300x200.webp 300w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-bigger-context-windows-are-not-always-better-768x512.webp 768w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-bigger-context-windows-are-not-always-better.webp 1379w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-709faaf elementor-widget elementor-widget-text-editor\" data-id=\"709faaf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Contextual information, in theory, creates better results because the model has access to additional data and should therefore perform better on the requests made. But this is sometimes not the case, with significant trade-off in performance because of the increased context.<\/p><p>Latency scales linearly with context size. Latency for a query of 500 tokens typically returns in milliseconds whilst a query of 500,000 tokens would usually take tens of seconds depending on your infrastructure. For real-time applications (e.g. customer support bot, code writing assistant, collaborative word processor), latency can be a deal-breaking constraint as opposed to an inconsequential hindrance.<\/p><p>Cost is more significant than latency, in fact one million tokens processed 10,000 times a day will create increased cost for computing resources than a well-designed RAG could produce at a fraction of those costs.<\/p><p>Quality is also affected by the serial position effect (whereby the first and last items in a list are remembered more easily than other items); therefore, a focused 8000 context that contains only the related content should perform better than the 128,000 context that contains all the related content but is buried with irrelevant content. Therefore, less data may produce a better quality outcome than more data.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1315380 elementor-widget elementor-widget-menu-anchor\" data-id=\"1315380\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"topic8\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8b80715 elementor-widget elementor-widget-heading\" data-id=\"8b80715\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Key takeaways and what to watch in 2026 and beyond<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9008fd3 elementor-widget elementor-widget-image\" data-id=\"9008fd3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/key-takeaways-about-context-window-1024x683.webp\" class=\"attachment-large size-large wp-image-4378\" alt=\"\" srcset=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/key-takeaways-about-context-window-1024x683.webp 1024w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/key-takeaways-about-context-window-300x200.webp 300w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/key-takeaways-about-context-window-768x512.webp 768w, https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/key-takeaways-about-context-window.webp 1379w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7d07737 elementor-widget elementor-widget-text-editor\" data-id=\"7d07737\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>\u2192\u00a0 A context window is temporary working memory \u2014 not persistent storage or true recall.<\/p><p>\u2192\u00a0 The serial position effect means middle-context information is structurally disadvantaged.<\/p><p>\u2192\u00a0 Massive context windows do not make RAG obsolete \u2014 cost, latency, and dilution keep RAG relevant.<\/p><p>\u2192\u00a0 KV caching helps performance but does not solve the cost problem of giant contexts.<\/p><p>\u2192\u00a0 Practical prompt design \u2014 position, chunking, summarization \u2014 beats raw context size for most use cases.<\/p><p>Looking ahead, the most exciting developments are not about raw context size \u2014 they are about smarter attention. Sparse attention mechanisms (like those used in models such as Longformer and BigBird) allow models to focus on the most relevant tokens rather than attending uniformly to all of them, dramatically improving both speed and middle-context accuracy. Memory-augmented architectures that maintain persistent state across sessions are also advancing rapidly, blurring the line between context and genuine long-term memory.<\/p><p>The future of context is not just longer \u2014 it is smarter. And understanding why today&#8217;s models forget is the first step to using them in a way that minimises the gaps. <br \/><br \/>Want to build a strong foundation in AI so these concepts come naturally? Start with our <span style=\"text-decoration: underline;\"><em><strong><a href=\"https:\/\/proleed.academy\/ai-and-machine-learning-training-course.php\">AI &amp; Machine Learning Training Course<\/a>.<\/strong><\/em><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-926b34d elementor-widget elementor-widget-menu-anchor\" data-id=\"926b34d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"topic9\"><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4c1ac02 elementor-widget elementor-widget-heading\" data-id=\"4c1ac02\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">FAQ \u2014 Common Questions<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e4ece79 elementor-widget elementor-widget-accordion\" data-id=\"e4ece79\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-accordion\">\n\t\t\t\t\t\t\t<div class=\"elementor-accordion-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2401\" class=\"elementor-tab-title\" data-tab=\"1\" role=\"button\" aria-controls=\"elementor-tab-content-2401\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon elementor-accordion-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon-closed\"><i class=\"fas fa-plus\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon-opened\"><i class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-accordion-title\" tabindex=\"0\">Why does ChatGPT have forgotten facts in middle of a chat?<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-2401\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"1\" role=\"region\" aria-labelledby=\"elementor-tab-title-2401\"><p>As long chats go, past information goes deep in the context window, and less attention is paid to it. Once the limit is exceeded, the older part may be deleted.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-accordion-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2402\" class=\"elementor-tab-title\" data-tab=\"2\" role=\"button\" aria-controls=\"elementor-tab-content-2402\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon elementor-accordion-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon-closed\"><i class=\"fas fa-plus\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon-opened\"><i class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-accordion-title\" tabindex=\"0\">What happens if there is no space in the context window?<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-2402\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"2\" role=\"region\" aria-labelledby=\"elementor-tab-title-2402\"><p>Either there will not be any input accepted or it might delete older information to accommodate new. Instructions must be placed early in the prompt.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-accordion-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2403\" class=\"elementor-tab-title\" data-tab=\"3\" role=\"button\" aria-controls=\"elementor-tab-content-2403\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon elementor-accordion-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon-closed\"><i class=\"fas fa-plus\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon-opened\"><i class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-accordion-title\" tabindex=\"0\">How many tokens can a 128K token model understand at one time?<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-2403\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"3\" role=\"region\" aria-labelledby=\"elementor-tab-title-2403\"><p>About 300 to 350 pages in normal text or around 96,000 English words. Code and tables consume more tokens than that.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-accordion-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2404\" class=\"elementor-tab-title\" data-tab=\"4\" role=\"button\" aria-controls=\"elementor-tab-content-2404\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon elementor-accordion-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon-closed\"><i class=\"fas fa-plus\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon-opened\"><i class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-accordion-title\" tabindex=\"0\">What is the difference between the context window and training data?<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-2404\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"4\" role=\"region\" aria-labelledby=\"elementor-tab-title-2404\"><p>Training data is something permanently stored by the model after learning. Context window contains information temporarily available to the model during conversations.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-accordion-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-2405\" class=\"elementor-tab-title\" data-tab=\"5\" role=\"button\" aria-controls=\"elementor-tab-content-2405\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon elementor-accordion-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon-closed\"><i class=\"fas fa-plus\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-accordion-icon-opened\"><i class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-accordion-title\" tabindex=\"0\">Does having large context windows mean using no RAG?<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-2405\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"5\" role=\"region\" aria-labelledby=\"elementor-tab-title-2405\"><p>No. Large context windows are helpful for single-document-based conversations while RAG still offers advantages for larger databases.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b30d0e7 elementor-widget elementor-widget-spacer\" data-id=\"b30d0e7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0bf6c48 elementor-widget elementor-widget-spacer\" data-id=\"0bf6c48\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Table of contents What is a context window in an LLM? How long-context LLMs work: 8K vs. 128K vs. 1M [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":4361,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[7,6],"tags":[],"class_list":["post-4359","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-business-analysis"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Context Windows Explained: Why Long- Context LLM&#039;s Still Forget Information - Proleed Academy<\/title>\n<meta name=\"description\" content=\"Discover how AI context windows work, why long-context LLMs still forget information, &amp; how tokens, attention, RAG &amp; prompt design impact AI memory, accuracy &amp; performance.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Context Windows Explained: Why Long- Context LLM&#039;s Still Forget Information - Proleed Academy\" \/>\n<meta property=\"og:description\" content=\"Discover how AI context windows work, why long-context LLMs still forget information, &amp; how tokens, attention, RAG &amp; prompt design impact AI memory, accuracy &amp; performance.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/\" \/>\n<meta property=\"og:site_name\" content=\"Proleed Academy\" \/>\n<meta property=\"article:published_time\" content=\"2026-05-18T05:21:24+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-05-25T04:41:33+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-long-context-llms-still-forget-information.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"960\" \/>\n\t<meta property=\"og:image:height\" content=\"640\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Aditya Sharma\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Aditya Sharma\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"13 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/context-windows-explained-why-long-context-llms-still-forget-information\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/context-windows-explained-why-long-context-llms-still-forget-information\\\/\"},\"author\":{\"name\":\"Aditya Sharma\",\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/#\\\/schema\\\/person\\\/b3961826c4e7ad0bfce9ad250dbe71ea\"},\"headline\":\"Context Windows Explained: Why Long- Context LLM&#8217;s Still Forget Information\",\"datePublished\":\"2026-05-18T05:21:24+00:00\",\"dateModified\":\"2026-05-25T04:41:33+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/context-windows-explained-why-long-context-llms-still-forget-information\\\/\"},\"wordCount\":2364,\"image\":{\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/context-windows-explained-why-long-context-llms-still-forget-information\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/why-long-context-llms-still-forget-information.webp\",\"articleSection\":[\"Artificial Intelligence\",\"Business Analysis\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/context-windows-explained-why-long-context-llms-still-forget-information\\\/\",\"url\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/context-windows-explained-why-long-context-llms-still-forget-information\\\/\",\"name\":\"Context Windows Explained: Why Long- Context LLM's Still Forget Information - Proleed Academy\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/context-windows-explained-why-long-context-llms-still-forget-information\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/context-windows-explained-why-long-context-llms-still-forget-information\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/why-long-context-llms-still-forget-information.webp\",\"datePublished\":\"2026-05-18T05:21:24+00:00\",\"dateModified\":\"2026-05-25T04:41:33+00:00\",\"author\":{\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/#\\\/schema\\\/person\\\/b3961826c4e7ad0bfce9ad250dbe71ea\"},\"description\":\"Discover how AI context windows work, why long-context LLMs still forget information, & how tokens, attention, RAG & prompt design impact AI memory, accuracy & performance.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/context-windows-explained-why-long-context-llms-still-forget-information\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/proleed.academy\\\/blog\\\/context-windows-explained-why-long-context-llms-still-forget-information\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/context-windows-explained-why-long-context-llms-still-forget-information\\\/#primaryimage\",\"url\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/why-long-context-llms-still-forget-information.webp\",\"contentUrl\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/why-long-context-llms-still-forget-information.webp\",\"width\":960,\"height\":640,\"caption\":\"why-long-context-llms-still-forget-information\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/context-windows-explained-why-long-context-llms-still-forget-information\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Context Windows Explained: Why Long- Context LLM&#8217;s Still Forget Information\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/#website\",\"url\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/\",\"name\":\"Proleed Academy\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/proleed.academy\\\/blog\\\/#\\\/schema\\\/person\\\/b3961826c4e7ad0bfce9ad250dbe71ea\",\"name\":\"Aditya Sharma\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/3b404633f7f814ff02f37ef486fc949d796fb1f3343832af04960ae5b032c2c2?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/3b404633f7f814ff02f37ef486fc949d796fb1f3343832af04960ae5b032c2c2?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/3b404633f7f814ff02f37ef486fc949d796fb1f3343832af04960ae5b032c2c2?s=96&d=mm&r=g\",\"caption\":\"Aditya Sharma\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Context Windows Explained: Why Long- Context LLM's Still Forget Information - Proleed Academy","description":"Discover how AI context windows work, why long-context LLMs still forget information, & how tokens, attention, RAG & prompt design impact AI memory, accuracy & performance.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/","og_locale":"en_US","og_type":"article","og_title":"Context Windows Explained: Why Long- Context LLM's Still Forget Information - Proleed Academy","og_description":"Discover how AI context windows work, why long-context LLMs still forget information, & how tokens, attention, RAG & prompt design impact AI memory, accuracy & performance.","og_url":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/","og_site_name":"Proleed Academy","article_published_time":"2026-05-18T05:21:24+00:00","article_modified_time":"2026-05-25T04:41:33+00:00","og_image":[{"width":960,"height":640,"url":"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-long-context-llms-still-forget-information.webp","type":"image\/webp"}],"author":"Aditya Sharma","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Aditya Sharma","Est. reading time":"13 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#article","isPartOf":{"@id":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/"},"author":{"name":"Aditya Sharma","@id":"https:\/\/proleed.academy\/blog\/#\/schema\/person\/b3961826c4e7ad0bfce9ad250dbe71ea"},"headline":"Context Windows Explained: Why Long- Context LLM&#8217;s Still Forget Information","datePublished":"2026-05-18T05:21:24+00:00","dateModified":"2026-05-25T04:41:33+00:00","mainEntityOfPage":{"@id":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/"},"wordCount":2364,"image":{"@id":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#primaryimage"},"thumbnailUrl":"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-long-context-llms-still-forget-information.webp","articleSection":["Artificial Intelligence","Business Analysis"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/","url":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/","name":"Context Windows Explained: Why Long- Context LLM's Still Forget Information - Proleed Academy","isPartOf":{"@id":"https:\/\/proleed.academy\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#primaryimage"},"image":{"@id":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#primaryimage"},"thumbnailUrl":"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-long-context-llms-still-forget-information.webp","datePublished":"2026-05-18T05:21:24+00:00","dateModified":"2026-05-25T04:41:33+00:00","author":{"@id":"https:\/\/proleed.academy\/blog\/#\/schema\/person\/b3961826c4e7ad0bfce9ad250dbe71ea"},"description":"Discover how AI context windows work, why long-context LLMs still forget information, & how tokens, attention, RAG & prompt design impact AI memory, accuracy & performance.","breadcrumb":{"@id":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#primaryimage","url":"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-long-context-llms-still-forget-information.webp","contentUrl":"https:\/\/proleed.academy\/blog\/wp-content\/uploads\/2026\/05\/why-long-context-llms-still-forget-information.webp","width":960,"height":640,"caption":"why-long-context-llms-still-forget-information"},{"@type":"BreadcrumbList","@id":"https:\/\/proleed.academy\/blog\/context-windows-explained-why-long-context-llms-still-forget-information\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/proleed.academy\/blog\/"},{"@type":"ListItem","position":2,"name":"Context Windows Explained: Why Long- Context LLM&#8217;s Still Forget Information"}]},{"@type":"WebSite","@id":"https:\/\/proleed.academy\/blog\/#website","url":"https:\/\/proleed.academy\/blog\/","name":"Proleed Academy","description":"","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/proleed.academy\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/proleed.academy\/blog\/#\/schema\/person\/b3961826c4e7ad0bfce9ad250dbe71ea","name":"Aditya Sharma","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/3b404633f7f814ff02f37ef486fc949d796fb1f3343832af04960ae5b032c2c2?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/3b404633f7f814ff02f37ef486fc949d796fb1f3343832af04960ae5b032c2c2?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/3b404633f7f814ff02f37ef486fc949d796fb1f3343832af04960ae5b032c2c2?s=96&d=mm&r=g","caption":"Aditya Sharma"}}]}},"_links":{"self":[{"href":"https:\/\/proleed.academy\/blog\/wp-json\/wp\/v2\/posts\/4359","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/proleed.academy\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/proleed.academy\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/proleed.academy\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/proleed.academy\/blog\/wp-json\/wp\/v2\/comments?post=4359"}],"version-history":[{"count":16,"href":"https:\/\/proleed.academy\/blog\/wp-json\/wp\/v2\/posts\/4359\/revisions"}],"predecessor-version":[{"id":4387,"href":"https:\/\/proleed.academy\/blog\/wp-json\/wp\/v2\/posts\/4359\/revisions\/4387"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/proleed.academy\/blog\/wp-json\/wp\/v2\/media\/4361"}],"wp:attachment":[{"href":"https:\/\/proleed.academy\/blog\/wp-json\/wp\/v2\/media?parent=4359"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/proleed.academy\/blog\/wp-json\/wp\/v2\/categories?post=4359"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/proleed.academy\/blog\/wp-json\/wp\/v2\/tags?post=4359"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}