Get The Context Engineering Field Guide

Context engineering is the skill of 2026 — engineering everything the model sees BEFORE it answers, so a mid model can outperform a frontier one. This field guide is the whole map in 8 pages: the one idea (the window is RAM, and RAM is finite), LangChain's four levers (WRITE, SELECT, COMPRESS, ISOLATE), the techniques the labs actually use with the receipts (compaction, tool-result clearing that cut context 335K->173K, sub-agents that beat single-agent by 90.2% but cost ~15x the tokens, memory, just-in-time retrieval, and the KV-cache pro move that's ~10x cheaper), why context fails (poisoning, distraction, confusion, clash + Lost-in-the-Middle and NoLiMa, where GPT-4o fell from 99% to ~70% at 32K), the 2026 state of the art, and a 10-point checklist plus a 3-question diagnostic you can run on any token. The principle under everything: find the smallest set of high-signal tokens that get the work done.

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