bmad-advanced-elicitation

You can improve LLM output quality by applying iterative refinement techniques like Socratic questioning and red-teaming to force structured reconsideration of a draft. This method-driven deep critique returns an enhanced version for your explicit acceptance.

Deep critique in prompt workflows loads elicitation methods from a registry, selects relevant approaches, and executes them in a controlled loop to systematically identify weaknesses and enhance generated text through iterative refinement.

Yes, you can apply red teaming and first-principles thinking to revise AI generated content. The system interactively selects these elicitation methods from a loaded registry and applies them to strengthen analysis and accuracy.

Trigger an advanced elicitation workflow that presents method options, select Socratic questioning from the list, and let the controlled loop execute revisions before returning the enhanced output for your review.

Iterative elicitation requires no external dependencies, but needs a pre-existing LLM draft to critique and access to a local methods registry file to load and select appropriate refinement techniques.

Avoid method-driven critique loops for straightforward tasks not requiring deep analysis or when a fast, single-pass response suffices, as iterative refinement adds execution time and interactive selection steps.