AI Interviews

Once the gaps and inconsistencies are known, the pipeline closes them by asking the people who know. AI generates the questionnaires and conducts the Q&A — interviews with the relevant stakeholders, who are often already visible in the underlying documentation and the code check-ins. The domain experts fill the holes the documents left; the knowledge workers curate the answers rather than rubber-stamp them; and the results are written back as explanatory metadata. The dependency is the whole point: AI can only interview well once it has data. It needs the captured metadata, the extracted structure, and the detected gaps to know what to ask — and it needs the rules, skills and instructions (themselves metadata: markdown with frontmatter) to know how to ask it. Point an AI at a blank room and it asks generic questions; give it the structured context and it asks the sharp, specific ones that actually move the model forward. Interviews are not step one; they are what becomes possible after capture and gap-hunting. The output feeds the feedback loop: every answer sharpens the metadata, and every sharpened model makes the next interview better. This is understanding before modeling done at scale — AI-generated questions, human-curated answers, all captured as metadata.