Action guide
Write System Prompts for AI Agents Like a Pro
When your system prompt is a novel and the agent still veers: what is already baked into models, and what to add so your instructions carry real signal.
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Why subscribe
Long prompts feel productive. Usually they fight what the model already assumes about helpfulness, safety hedges, and formatting. Then you debug your own wallpaper.
For: Engineers shipping tool-using agents who need reliability without token soup.
- A framework for prompts that add signal, not repetition
- A map of baked-in behaviors so you stop compensating twice
- Guardrails that cost fewer tokens and fewer surprises
- Reusable prompt skeleton with annotated fixes
- Before/after mistakes that waste tokens weekly
- A production checklist you can run before merge
- Anchors in what actually breaks in prod, not literary prizes
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What you’ll learn
Which behaviors are already strong priors-helpfulness from RLHF, caution and disclaimers from safety training, charitable interpretation of messy user text, and format muscle memory from the pretraining distribution-so you stop wasting tokens repeating them, and where you still need explicit instructions (scope, tools, output shape) for agent work.
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