Harness Engineering - Part 5: Context Engineering

<p><strong>Part 2 of Practical AI Engineering: Beyond the Demo</strong></p> <p>Bigger context windows create a tempting idea:</p> <blockquote> <p>Put everything in. Let the model

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<p>Claude's context window holds 200,000 tokens. Gemini's stretches to two million. But response quality starts degrading long before the window fills up. Window size doesn't

<p><em>We stopped measuring retrieval quality by how many tokens we could fit into the prompt.</em></p> <p>When long-context models became available, many of us made the same

<p>For the last few years, building with large language models often started with one question:</p> <p><strong>“What prompt should I give the model?”</strong></p> <p>Developers

<p><em>Welcome back to the Harness Engineering series — a 10-part journey from raw language model to production-ready agentic system. Made by builders. For builders.</em></p>

<p>Imagine you have a super-smart assistant. This assistant can write stories, create images, or even help plan your day. All you have to do is tell it what you want. You don't

<p><em>Welcome back to the Harness Engineering series — a 10-part journey from raw language model to production-ready agentic system. Made by builders. For builders.</em></p>