AI Applications In Podcast Production

AI has completely changed how our agency creates hooks for podcast clips. Instead of staring at a blank screen struggling to think of the perfect opener, we now have AI generate 10 different hook options at once. Then we pick the hook that feels right for the audience and modify it to match the creator's authentic voice. The human touch remains essential. And the genius of this method isn't letting AI write the final hook, you're just using AI as a springboard for your own creativity. Furthermore, what I love most is how this approach saves mental energy for the parts of content creation where human judgment truly matters, like deciding which moments from a 2-hour podcast deserve to become clips in the first place. So I want you to try this: Next time you're stuck on a hook, ask AI for multiple options, then trust your gut on which one resonates best with your specific audience.

I built a research assistant to streamline my podcast preparation process. For each episode, I create a research brief with my insights, guest background, topic context, and potential questions. This involves researching the guest and their company, reviewing their podcasts, reading their blog posts, and diving into the discussion topic—quite a time-consuming and effort-intensive process. To save time, I built an agent to handle this work. The project also showcases how to design an event-driven AI architecture, decoupling AI workflows from the app stack, leveraging event streams for data sharing and orchestration, and incorporating real-time data. It's built with: ◆ OpenAI various versions of GPT and Whisper ◆ LangChain for prompt templates and LLM API abstraction ◆ Next.js by Vercel ◆ Kafka and Flink on Confluent Cloud for agent orchestration and stream processing ◆ Bootstrap and good ol' fashion hand coded CSS for styling Behind the scenes: 1. Create a podcast research bundle with the guest name, topic, and source URLs 2. The web app writes the research request to an application database 3. A source connector pulls the data into a Kafka topic and kick starts the agentic workflow 4. All URLs are processed, text is chunked, and embeddings created and synced to a vector database 5. Flink and GPT is used to pull potential questions from the source materials 6. A secondary agent compiles all the research material into a research brief I cover this in detail here: https://lnkd.in/gSSBuC3t You can checkout the code here: https://lnkd.in/gUpY-YgQ #llms #agenticai #kafka #flink #confluentcloud