<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Context Window Review</title><description>Context Window Review is an independent publication on applied AI: evals, retrieval, agents, latency and cost, written for engineers, PMs and designers shipping AI features.</description><link>https://sitemoment.com/demo/minimal-context-window-review/</link><language>en</language><item><title>Q&amp;A: designing AI features that users forgive when they are wrong</title><link>https://sitemoment.com/demo/minimal-context-window-review/posts/designing-ai-features-users-forgive/</link><guid isPermaLink="true">https://sitemoment.com/demo/minimal-context-window-review/posts/designing-ai-features-users-forgive/</guid><description>A design lead on why her team stopped chasing accuracy alone, the edit-rate metric they track instead, and the small interface changes that halved complaints.</description><pubDate>Wed, 28 Oct 2026 00:00:00 GMT</pubDate><category>Product and design</category><category>Agents</category><author>Inês Valente</author></item><item><title>Latency budgets for chat interfaces: spend 1.5 seconds on purpose</title><link>https://sitemoment.com/demo/minimal-context-window-review/posts/latency-budgets-for-chat-uis/</link><guid isPermaLink="true">https://sitemoment.com/demo/minimal-context-window-review/posts/latency-budgets-for-chat-uis/</guid><description>Users judge a chat feature in the first second and a half. How to split that budget across network, guardrails, retrieval and the model, and what to show while they wait.</description><pubDate>Thu, 24 Sep 2026 00:00:00 GMT</pubDate><category>Product and design</category><category>Cost and latency</category><author>Inês Valente</author></item><item><title>Prompt caching math: when it saves 75% and when it costs you more</title><link>https://sitemoment.com/demo/minimal-context-window-review/posts/prompt-caching-math/</link><guid isPermaLink="true">https://sitemoment.com/demo/minimal-context-window-review/posts/prompt-caching-math/</guid><description>Cached prefixes are cheap to read and slightly expensive to write. We work out the break-even hit rate, the traps that keep it at zero, and a prompt layout that helps.</description><pubDate>Thu, 27 Aug 2026 00:00:00 GMT</pubDate><category>Cost and latency</category><category>Agents</category><author>Tobi Adeyemi</author></item><item><title>Agents need an undo button before they need more autonomy</title><link>https://sitemoment.com/demo/minimal-context-window-review/posts/agents-need-undo/</link><guid isPermaLink="true">https://sitemoment.com/demo/minimal-context-window-review/posts/agents-need-undo/</guid><description>An agent that is right 97% of the time still makes dozens of wrong moves a day. Reversibility, not accuracy, makes that tolerable. An argument, with a pattern.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate><category>Agents</category><category>Product and design</category><author>Inês Valente</author></item><item><title>Postmortem: why our RAG answers drifted when nobody deployed anything</title><link>https://sitemoment.com/demo/minimal-context-window-review/posts/why-rag-answers-drift/</link><guid isPermaLink="true">https://sitemoment.com/demo/minimal-context-window-review/posts/why-rag-answers-drift/</guid><description>Over six weeks an internal help assistant got quietly worse with no model or prompt change. The causes were all in the retrieval layer, and all preventable.</description><pubDate>Thu, 16 Jul 2026 00:00:00 GMT</pubDate><category>Retrieval and RAG</category><category>Evals</category><category>Field reports</category><author>Mara Ekström</author></item><item><title>How to read a model card skeptically, line by line</title><link>https://sitemoment.com/demo/minimal-context-window-review/posts/reading-a-model-card-skeptically/</link><guid isPermaLink="true">https://sitemoment.com/demo/minimal-context-window-review/posts/reading-a-model-card-skeptically/</guid><description>Model cards are marketing and documentation at once. A practical checklist for separating the claims you can rely on from the ones you need to test yourself.</description><pubDate>Tue, 30 Jun 2026 00:00:00 GMT</pubDate><category>Small and on-device</category><category>Evals</category><author>Kenji Arai</author></item><item><title>Field test: three small models, two laptops and 200 invoices</title><link>https://sitemoment.com/demo/minimal-context-window-review/posts/small-models-on-a-laptop-field-test/</link><guid isPermaLink="true">https://sitemoment.com/demo/minimal-context-window-review/posts/small-models-on-a-laptop-field-test/</guid><description>We ran 3B, 7B and 14B open-weight models on two ordinary laptops to extract invoice fields. Speed, accuracy, memory and battery, measured rather than guessed.</description><pubDate>Thu, 11 Jun 2026 00:00:00 GMT</pubDate><category>Small and on-device</category><category>Field reports</category><category>Cost and latency</category><author>Kenji Arai</author></item><item><title>The real cost of long context: a worked example at 40,000 calls a day</title><link>https://sitemoment.com/demo/minimal-context-window-review/posts/the-real-cost-of-long-context/</link><guid isPermaLink="true">https://sitemoment.com/demo/minimal-context-window-review/posts/the-real-cost-of-long-context/</guid><description>Stuffing 120,000 tokens into every request is easy to build and expensive to run. We price it, time it and compare it against a 6,000-token retrieval setup.</description><pubDate>Thu, 21 May 2026 00:00:00 GMT</pubDate><category>Cost and latency</category><category>Retrieval and RAG</category><author>Tobi Adeyemi</author></item><item><title>What an eval suite actually catches (and the three things it never will)</title><link>https://sitemoment.com/demo/minimal-context-window-review/posts/what-an-eval-suite-catches/</link><guid isPermaLink="true">https://sitemoment.com/demo/minimal-context-window-review/posts/what-an-eval-suite-catches/</guid><description>A 240-case eval suite for a ticket summarizer, a cheaper model swap, and an honest accounting of which failures the suite caught and which reached users.</description><pubDate>Thu, 07 May 2026 00:00:00 GMT</pubDate><category>Evals</category><category>Field reports</category><author>Mara Ekström</author></item></channel></rss>