Title: Context Engineering for Python Codebases – Real Python
Open Graph Title: Context Engineering for Python Codebases – Real Python
Description: Learn how context engineering shapes what your AI coding agent sees on every turn, and use four practical strategies to keep your Python projects on track.
Open Graph Description: Learn how context engineering shapes what your AI coding agent sees on every turn, and use four practical strategies to keep your Python projects on track.
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"text": "\u003cp\u003e\u003ca href=\"/ref/ai-coding-glossary/context-engineering/\" class=\"ref-link\"\u003eContext engineering\u003c/a\u003e is the practice of curating what goes into an AI agent\u0026rsquo;s limited context window on each turn. It covers everything the agent sees alongside your prompt, including instruction files, opened files, tool definitions, and conversation history.\u003c/p\u003e"
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"text": "\u003cp\u003eAn \u003ccode\u003eAGENTS.md\u003c/code\u003e file is a repository-level instruction file that AI coding agents automatically load into the context window. It\u0026rsquo;s a good home for project conventions like the Python version, the dependency manager, the test command, and style rules. Keep it short to avoid diluting individual rules.\u003c/p\u003e"
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"text": "\u003cp\u003eThe four strategies are Curate, Distill, Delegate, and Externalize. Curate decides what to put in the window for the current task. Distill shrinks what\u0026rsquo;s already there. Delegate splits work across separate windows using subagents. Externalize saves information outside the window in files.\u003c/p\u003e"
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