Stop Prompting — Greg Pstrucha, Sentry
The talk argues that relying on manual re-prompting to correct AI-generated code is inefficient because agents lack memory and will repeat elementary mistakes. The core thesis is to stop prompting and start codifying rules and policies into deterministic checks. The speaker emphasizes that modern tooling, particularly linting, makes it cheap and easy to enforce complex conventions, such as keeping API endpoints synchronized with the OpenAPI schema, thereby raising the quality floor for AI-assisted development.
Key takeaways
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Stop Prompting, Start Codifying
0:02
Instead of repeatedly correcting an agent's mistakes (e.g., overly defensive code, unnecessary endpoints), developers should codify the desired rules and policies into the codebase to ensure deterministic quality.
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The Power of Linting
0:04
Linting is presented as the primary mechanism to codify rules. Historically, writing linters was costly, but with modern tools, it is cheap and easy, allowing developers to enforce conventions that prevent repeated agent errors.
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Mining for Rules
0:05
A practical strategy is to mine past transcripts, GitHub PR reviews, and bot feedback to identify high-quality, deterministic rules that can be enforced via linting.
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Handling Qualitative Measures
0:12
For rules that cannot be expressed deterministically (e.g., 'too many states' in a state machine), policies must be established. This can be managed using a 'Grumpy Engineer' review loop, which runs sub-agents against defined policies.