Splitting plan/implement/audit across different AI models — worth it?
A developer runs a three-stage workflow where different AI models handle different roles in a coding task. Planning goes to a stronger model, is handled by a lighter, faster model running in a high-reasoning mode, and the final audit (checking the code for problems) goes back to the stronger model. The reasoning is that having the same model audit its own code defeats the purpose, since it likely shares the same blind spots that caused any issues in the first place.
Because one tool offers generous and , the developer wants to use it for most of the work and only bring in a separate high-tier model for a final audit pass. They're asking whether others use a similar setup and looking for tips on getting the most out of a plan-with-strong-model, implement-with-light-model, audit-with-strong-model pipeline.
Key points
- Three-stage workflow: strong model plans, lightweight model implements, strong
- Motivation: a model auditing its own code may miss the same issues it originally introduced
- Question centers on whether using a credit-rich tool for most work plus a separate model only for the final audit is worth it
- Asking the community for similar setups and tips