How I Stared at Transcripts and Built an AI Pipeline Instead

说明与用途

使用 Cloudflare Workers 和 Llama‑3‑8b‑instruct 的边缘 AI 流水线自动生成播客标题、节目笔记和 SEO 关键词，将后期制作时间从数小时缩短到秒级。

问题

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

结果

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.

工作流

• 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

证据

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.

来源

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.

来源