ChatGPT Training for Podcasters with Parikshit Khanna
Updated: 7 days ago
AI-generated editorial cover with symbolic imagery. Independent article; no endorsement implied.
By Parikshit Khanna · Updated 7 October 2026
Parikshit Khanna’s podcast-focused AI training can help a team turn research and recordings into organised, reviewable production assets. The session is planned around the programme’s audience and workflow rather than a promise to automate editorial judgment.
From research to a published episode
The practical goal is to improve the process while keeping the source recording authoritative.
Workshop task | Reviewable output |
|---|---|
Define the episode | Audience brief and editorial objective |
Prepare questions | A research-led interview outline |
Organise a transcript | Sections with source timestamps |
Draft show notes | An outline checked against the recording |
Plan clips | Candidate moments with surrounding context |
Prepare publication | A checklist for claims, credits and disclosures |
Why this matters in 2026
Podcast discovery now includes interviews, narrative shows and regional-language catalogues. A production team needs useful full episodes and accurate promotional material. The companion trends article separates current editorial observations from Spotify’s completed 2025 chart.
A practical exercise
Work from a short public recording or material the team is authorised to use. Draft a summary, then compare every name, number and quotation with the source. Mark uncertainty instead of allowing the tool to fill it with a plausible invention.
About the trainer
Parikshit Khanna founded Digital Training Jet and works in corporate AI and prompt-engineering training. His portfolio includes TEDx speaking and visiting-faculty work at GLBIMR. He reports a professional reach of 3,50,000+ people; this is not a count of participants in this specific programme.
Who the session can serve
Podcast hosts, researchers, editors, communications teams and learning professionals can adapt the exercises to their role. The agenda can account for existing tools, approved data practices and the amount of review required.
Arrange a suitable workshop
Share the programme’s format, language, team size and main bottleneck. An agenda and quotation can then be discussed. No fixed event date, guaranteed audience growth or included software subscription is claimed here.
A practical podcast production exercise
Begin with a short recording or transcript that you have permission to use. Ask the AI to identify the main themes, propose chapter titles and draft a concise episode description. Require every suggested claim to be supported by the transcript, then compare the draft with the actual conversation.
Next, create several promotional captions with different lengths while preserving the guest’s meaning. Review names, quotations, timings and any sensitive statements before publication. This exercise turns a general interest in AI into a repeatable editorial workflow, with the producer retaining responsibility for what is released.
Reader question
Can AI invent quotations or missing guest answers to improve an episode?
No. Quotations should be traceable to the recording or transcript. Missing material should remain missing or be requested from the guest, rather than fabricated as part of the conversation.
Sources and further reading
Related reading
About Parikshit Khanna
Parikshit Khanna is the founder of Digital Training Jet, a corporate AI and prompt-engineering trainer, TEDx speaker and visiting faculty at GLBIMR. He reports a professional reach of 3,50,000+ people. His workshops connect practical AI use with clear communication and human review.
For workshops, speaking and collaborations: parikshitkhanna@digitaltrainingjet.com.
Independent editorial content. Mentioning a person, programme or product does not imply an endorsement or partnership with Parikshit Khanna or Digital Training Jet.
