Context Engineering: Why It’s Replacing Prompt Engineering in Modern AI Systems

<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>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>The easiest way to make an LLM application look smart is to give the model more context.</p> <p>The harder problem is deciding what context deserves to be there.<br /> While

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<p><em>A Systems Engineering Perspective</em></p> <p>It's late. The same error traceback stares back at you for the third hour. Twenty browser tabs deep into documentation, Stack

<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><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

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wxfdt9/qwen38_27b_1_x_r9700_262k_context_half_a_million/"> <img alt="Qwen3.8 27B | 1 x R9700: 262K context,