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<title>Stijn ter Steege – Notes on software and AI</title><link>https://stijntersteege.com/</link><description>Short, practical pieces on building software that lasts, and on using AI without the hype.</description><language>en</language>
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<item><title>Evals are the unit tests of AI features</title><link>https://stijntersteege.com/evals-are-the-unit-tests-of-ai-features/</link><guid>https://stijntersteege.com/evals-are-the-unit-tests-of-ai-features/</guid><pubDate>Wed, 21 Oct 2026 08:00:00 GMT</pubDate><description>How to test LLM features with a small, honest evaluation set: what to put in it, how to score it, and why it pays off the first time you change a prompt or a model.</description></item>
<item><title>Design your AI feature for the wrong answer</title><link>https://stijntersteege.com/design-your-ai-feature-for-the-wrong-answer/</link><guid>https://stijntersteege.com/design-your-ai-feature-for-the-wrong-answer/</guid><pubDate>Wed, 21 Oct 2026 08:00:00 GMT</pubDate><description>A better prompt won’t make a language model right all the time. Good AI features are designed around the wrong answer: confidence, review steps, fallbacks and a way back to a human.</description></item>
<item><title>Start with a modular monolith. Split later, if ever.</title><link>https://stijntersteege.com/start-with-a-modular-monolith/</link><guid>https://stijntersteege.com/start-with-a-modular-monolith/</guid><pubDate>Wed, 21 Oct 2026 08:00:00 GMT</pubDate><description>Why a modular monolith is the right starting architecture for most products: clear module boundaries, one deployment, and an honest path to services when you actually need them.</description></item>
<item><title>Review the intent, not the diff</title><link>https://stijntersteege.com/review-the-intent-not-the-diff/</link><guid>https://stijntersteege.com/review-the-intent-not-the-diff/</guid><pubDate>Wed, 21 Oct 2026 08:00:00 GMT</pubDate><description>AI coding assistants make pull requests bigger and smoother. How to change code review so it still catches what matters: intent, boundaries, tests and risk.</description></item>
<item><title>RAG is a search problem before it is an AI problem</title><link>https://stijntersteege.com/rag-is-a-search-problem-first/</link><guid>https://stijntersteege.com/rag-is-a-search-problem-first/</guid><pubDate>Wed, 21 Oct 2026 08:00:00 GMT</pubDate><description>Most disappointing retrieval-augmented generation (RAG) projects fail at retrieval, not generation. Practical steps to fix the search part first: clean sources, hybrid search, metadata and measuring recall.</description></item>
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