Context engineering: give the model the right information, not more of it

When people talk about getting better results from AI, they usually mean better prompts. In my experience the prompt is the smallest part. What matters more is everything else the model sees when it starts working: which files, which rules, which past decisions, and, just as important, what it does not see.

Anthropic's engineering team calls this context engineering (opens in a new tab), and describes the goal well: find the smallest set of high-signal information that makes the right outcome likely. More context is not better context. A model given everything tends to latch onto the wrong thing, the same way a new teammate handed the whole wiki on day one would.

What it looks like in practice

Short rules, close to the code. In the Invictus codebase, every part of the system has a short file that says what it holds and what it must not do, plus one file of rules for the whole repository. They bind people and AI tools alike. When an assistant opens the indexer, it reads the indexer's rules, not a 40-page document about everything.

A reading order, not a library. My personal notes vault tells an assistant exactly what to read first and in what order, and to ask rather than guess when something is missing. I wrote about it in a shared brain for me and my AI tools.

Reusable skills instead of repeated explanations. I keep a small collection of agent skills, each one a folder with a single instruction file: how I write commit messages, how I want Playwright tests structured, how to review prose. Instead of explaining the same thing in every session, the tool loads the skill when the task needs it.

Make your own product readable. This site serves every page as plain Markdown, an index at /llms.txt, and structured data for agents. That is context engineering pointed outward: making it easy for someone else's AI to get you right.

What I avoid

• Pasting entire codebases or documents "just in case".
• Long instructions that repeat what the code already makes obvious.
• Rules that live only in my head, which the tool can never see.
• Letting old, wrong notes pile up. Stale context is worse than missing context, because it sounds confident.

Further reading

• Anthropic, Effective context engineering for AI agents (opens in a new tab).
• AGENTS.md (opens in a new tab), a simple open format for instructions that coding agents read.
• Simon Willison, Here's how I use LLMs to help me write code (opens in a new tab), especially the part on context.