How I Built a Lightweight Edge AI Pipeline for Podcasts

说明与用途

为播客构建轻量级边缘 AI 流水线，使用 LLM 生成节目笔记和标题，提升后期制作效率。

问题

Post‑recording tasks—creating show notes, titles, and timestamps—drained indie podcasters and were hard to automate with existing AI tools.

结果

The pipeline produced concise, structured show notes and compelling titles with minimal latency, freeing the creator from tedious manual editing.

工作流

• 1. Record podcast and export transcript
• 2. Send transcript to Cloudflare Worker
• 3. Worker calls @cf/meta/llama-3-8b-instruct with constrained system prompt to produce markdown show notes
• 4. Worker returns JSON with episode summary, key takeaways, timestamps, resources
• 5. Separate title generator builds prompt with tone and count, calls LLM, returns numbered list of titles
• 6. Podcaster reviews and publishes metadata

证据

I looked at existing AI tools to help with this, but they were either generic text spinners that hallucinated timestamps, or massive enterprise suites priced for media companies, not solo developers.

来源

Here is the core of the show notes generation pipeline. I pass the raw transcript and use a highly constrained system prompt to force the model into a specific markdown structure:

来源

Breaking the pipeline into smaller, constrained prompts worked much better than my initial attempt to do everything in one giant prompt.

来源