bmad-advanced-elicitation

To improve AI-generated text through iterative refinement, apply deep critique methods like socratic, first principles, pre-mortem, and red team analysis. This process systematically challenges and enhances the initial AI output to provide a more comprehensive and deeper analysis.

Deep critique methods like socratic questioning, first principles, pre-mortem, and red team analysis work best for deepening AI text analysis. These iterative refinement techniques identify logical flaws and enhance the overall quality and depth of the generated content.

Yes, you can use the red team critique method to refine AI output. This approach functions as an iterative refinement technique where the system challenges the text to find vulnerabilities and weaknesses, ultimately enhancing the quality of the generated content.

To perform a pre-mortem analysis on AI-generated content, use an iterative refinement skill that applies this specific critique method. The system evaluates the text by assuming the output has failed, identifying potential flaws in advance to provide a deeper analysis and improved quality.

Yes, you need a methods.csv file containing the elicitation methods to start refining AI text. The system requires this file to apply the iterative critique techniques and also supports a party-mode configuration utilizing agent rosters for enhanced analysis.

You should avoid using iterative refinement for AI text critique when you only need a quick, single-pass response without deeper analysis. Because this process applies multiple critique methods to enhance quality, it is designed for comprehensive improvement rather than rapid, shallow text generation.