Building an AI Podcast Workflow: My Retrospective and Mistakes

Description and use

Automating podcast post‑production with Edge‑AI on Cloudflare Workers reduces admin overhead and boosts SEO visibility.

Problem

Podcast post‑production is time‑consuming and hampers discoverability, causing creator burnout.

Outcome

The edge AI workflow cuts post‑production time, improves title relevance and show‑note structure, and enhances discoverability, thereby reducing creator burnout.

Workflow

• 1. Record episode and export audio
• 2. Generate transcript (outside scope)
• 3. Send transcript to Cloudflare Worker
• 4. Worker calls @cf/meta/llama-3-8b-instruct with structured system prompt
• 5. Receive JSON with formatted show notes
• 6. Use separate title generator function to produce numbered title list
• 7. Publish episode with generated title and show notes

Evidence

I was spending 80% of my time on administrative overhead and only 20% on actual creative content.

Source

I decided to build the entire platform on Cloudflare Workers, utilizing AI inference at the edge.

Source

The latency is incredibly low, meaning users get their generated titles and show notes almost instantaneously, regardless of where they are in the world.

Source