From Transcript to Show Notes: My AI Pipeline Explained

I'll never forget the day I realized my podcast was struggling not because of bad audio, but because of bad packaging. I had just spent three hours editing a fantastic 30-minute interview. The conversation was gold. But then came the part I dreaded: writing the show notes and agonizing over the episode title. I stared at a blank screen for forty minutes, trying to write a title that was catchy but not cheesy. By the time I published, I was completely burnt out. The admin work of podcasting was killing my motivation. So, I decided to build something to automate the heavy lifting.

When I started building an AI pipeline for podcast metadata, I faced a few key architectural decisions. The first was whether to use a single, massive prompt to generate everything at once, or to break it down into a chained pipeline. I initially tried the single prompt approach. It was a disaster. The AI would get confused about formatting, mix up the title constraints with the show notes structure, and hallucinate timestamps.

I pivoted to a modular, chained architecture. Each task—title generation, show notes, SEO keywords—got its own dedicated API call with a highly specific system prompt. The second major decision was how to handle tone and constraints. Generic prompts like "Write a good title" yield generic, robotic results. I realized I needed to enforce strict rules within the system prompt, explicitly defining word counts, formatting, and negative constraints (what not to do). I built the backend using Node.js and Python for the prompt engineering, keeping the frontend lightweight so the focus remained on the output quality.

Let’s look at the actual code that powers the title generation. The secret sauce isn't just calling the API; it's how the prompt is structured. Here is the Python function I use to generate podcast titles:

def generate_podcast_titles(topic, tone="engaging", count=5):
    system_prompt = """You are an expert podcast producer. Generate {count} compelling episode titles.
Rules:
- Each title should be 5-10 words
- Use curiosity gaps, numbers, or strong claims
- Match the tone: {tone}
- Avoid clickbait that overpromises
- Format as a numbered list
""".format(count=count, tone=tone)

    user_prompt = f"""Episode topic: {topic}

Generate {count} title options that would make someone want to click play."""

    return [system_prompt, user_prompt]

# Example usage:
sys_prompt, user_prompt = generate_podcast_titles(
    topic="How to start a podcast with zero budget",
    tone="inspirational",
    count=5
)

Notice the explicit rules in the system_prompt. Telling the AI to "avoid clickbait that overpromises" is just as important as telling it to be engaging. Without that negative constraint, the AI tends to generate titles like "This One Trick Will Make You a Millionaire Podcaster!" By enforcing a 5-10 word limit, we keep the titles punchy and readable on mobile devices.

For the show notes, the architecture shifts slightly. We need to parse a long transcript and extract structured data. Here is the JavaScript function handling the OpenAI API call for show notes:

async function generateShowNotes(transcript, apiKey) {
  const response = await fetch('https://api.openai.com/v1/chat/completions', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${apiKey}`,
    },
    body: JSON.stringify({
      model: 'gpt-4o-mini',
      messages: [
        {
          role: 'system',
          content: `You are a podcast show notes writer. 
Structure output as:
## Episode Summary
(2-3 sentences)

## Key Takeaways
- bullet list of 5-7 main points

## Timestamps
- [00:00] Topic intro
- [05:30] First main section
etc.

## Resources Mentioned
- list of links / books / tools`
        },
        {
          role: 'user',
          content: transcript
        }
      ],
      temperature: 0.7,
      max_tokens: 800,
    })
  });

  const data = await response.json();
  return data.choices[0].message.content;
}

The key here is the strict markdown structure defined in the system message. By forcing the AI to output specific headings, we can easily parse the response on the frontend and render it beautifully. I also set the temperature to 0.7. For show notes, you want a bit of creativity in the summary, but not so much that it hallucinates facts from the transcript. Setting max_tokens to 800 keeps the response concise and prevents the model from rambling.

Building this pipeline taught me a lot about prompt engineering. What worked incredibly well was the modular approach; isolating tasks meant I could tweak the title generator without accidentally breaking the show notes format. What surprised me was how much the AI relies on negative constraints. It naturally wants to be overly enthusiastic, so explicitly telling it what to avoid yielded much more natural, human-sounding copy. If I were to start over, I’d implement a streaming response for the show notes. Waiting 15 seconds for a massive block of text to generate feels slow, even if the final output is high quality. Streaming the text as it generates would vastly improve the perceived performance.

If you're a podcaster struggling with the post-production admin, or a developer looking to build similar AI tools, I've packaged these exact workflows into a suite called PodCrisp. It includes a free AI podcast title generator, an AI show notes generator, a description writer, an episode script outline generator, and SEO keyword suggestions.

You can try the free podcast title generator right here: https://podcrisp.com/free-tools/title-generator. It uses the exact prompt structure I walked through above to give you click-worthy, non-cheesy titles in seconds. Give it a spin on your next episode and see if it saves you some time.

---

Full disclosure: I'm the developer of PodCrisp. I built this tool because I needed it myself.