A podcast host recently told me that he prepares questions from the guest’s bio using ChatGPT. That works for the basics, but a bio does not show which stories the guest has repeated across other interviews or which questions they have already answered many times.
I’m helping Audiogram test a different workflow. It connects to Claude through MCP, searches Apple Podcasts, retrieves available episode transcripts, and lets Claude compare the guest’s previous answers before drafting new questions.
For one test, I used two published Sam Altman interviews. The workflow pulled both available transcripts, separated recurring themes from open gaps, and produced follow-up questions around measurable evidence, privacy limits, and independent review—rather than repeating another general “will AI be good or bad?” question.
The prompt is simple:
Prepare an interview brief for [guest] about [interview angle].
Find podcast episodes where the guest is actually interviewed, retrieve the available transcripts, and compare them. Show recurring themes, changes in position, questions already answered, and five follow-up questions based on gaps or unsupported claims.
Cite the podcast and episode for every finding. Separate transcript evidence from inference, and say what is missing when the available material is not enough.
This is for research across published Apple Podcasts episodes. It is not a raw-audio editor, and transcript availability and speaker labels still need to be checked.
You can see the complete recipe and tested example here:
Podcast guest interview preparation with Audiogram
If you prepare podcast interviews, would previous-interview comparison improve your questions, or is another part of guest research still the bigger problem?
Disclosure: I’m helping Audiogram with early-user growth and used AI to help edit this post.