How I Stared at Transcripts and Built an AI Pipeline Instead

Description and use

An edge AI pipeline on Cloudflare Workers using Llama‑3‑8b‑instruct automates title, show‑note, and keyword generation from podcast transcripts, cutting post‑production time to seconds.

Problem

Podcast creators spend excessive time converting raw transcripts into show notes, titles, and SEO keywords, leading to burnout and inconsistent publishing.

Outcome

The pipeline reduces post‑production time from hours to seconds, improves title quality through prompt engineering, and integrates SEO keyword suggestions, enabling consistent and efficient publishing.

Workflow

• 1. Capture raw transcript from recording
• 2. Send transcript to Cloudflare Worker
• 3. Worker calls Llama‑3‑8b‑instruct with structured system prompt to generate show notes in Markdown
• 4. Worker returns JSON with episode summary, key takeaways, timestamps, resources, and SEO keywords
• 5. Frontend parses Markdown headings for display
• 6. Optional title generation via buildTitlePrompt function with tone and count parameters

Evidence

I was struggling with the post‑production admin. Staring at 45‑minute raw transcripts, trying to pull out timestamps, writing show notes, and brainstorming titles was taking me three hours per episode.

Source

Using the edge AI model means I don't have to manage GPU infrastructure. The system prompt forces the LLM to output strict Markdown headings, so the frontend can parse the response directly.

Source