The practical implementation involves thinking about the conversation your audience wants to have rather than the keywords they might type. What are they trying to accomplish? What confuses them? What decisions are they facing? What objections or concerns do they have? When you address these elements in natural, conversational language, you simultaneously create content that people find valuable and that AI models recognize as comprehensive answers to common questions.
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I then added a few more personal preferences and suggested tools from my previous failures working with agents in Python: use uv and .venv instead of the base Python installation, use polars instead of pandas for data manipulation, only store secrets/API keys/passwords in .env while ensuring .env is in .gitignore, etc. Most of these constraints don’t tell the agent what to do, but how to do it. In general, adding a rule to my AGENTS.md whenever I encounter a fundamental behavior I don’t like has been very effective. For example, agents love using unnecessary emoji which I hate, so I added a rule:
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