Mastering AI Tools for Podcast Hosts

Google AI Studio and Gemini are my go-to tools when it comes to transcribing podcast interviews and formatting them so they’re worthy of a blog post and newsletter. Here is my exact workflow, with the prompts I used for my most recent podcast. I upload the MP3 to Google AI Studio, which excels at handling audio files. My prompt: "The attached file is an interview for a podcast called A History of Marketing between Andrew Mitrak and Sergio Zyman, Chief Marketing Officer of Coca Cola. It is about the history of New Coke, the Cola Wars in the 1980s and early 1990s. Please generate a clean transcript and remove "um" and other filler words and accidentally repeated words but otherwise be as accurate as possible." Providing context in the prompt (names, topic) makes for much more accurate output. I review the transcript in Google Docs using its error-checking features. I then upload this version of the transcript to my YouTube video, which is a big improvement over its auto-subtitles. Next, I use the Gemini App. I attach a PDF of journalistic transcribing instructions and use this prompt followed by the full text of the transcript: "The following is an interview transcript. Please make edits to correct grammar and remove false starts, following the attached transcribing instructions. Please format this for a blog and add line breaks when speakers alternate. When there is a long answer, break it up as needed into separate paragraphs for readability. Put the names of speakers in front of their dialogue each time they speak and bold their names." This cleans up the text, adds formatting, and attributes dialogue. The output at this point looks a lot like a blog post! I export to Google Docs. A 30-minute interview will be about 10 pages. For SEO and scannability, I use this prompt: "Please suggest SEO-optimized headers to add to this blog. Make them descriptive of sections. Keep them short, but don't try to be cute. Make sure they improve scannability. Use H2 and H3 formats." This generates headers I insert into the blog. I rewrite and edit these, but AI saves a lot of time here with the first draft. Finally, I review the blog post while listening to the MP3. This lets me check both the transcript and the audio file for errors simultaneously. At 2X speed this process takes 15-30 minutes. This workflow with Google AI Studio and Gemini has streamlined my post-interview process. It's not just about saving time, it's about producing something I otherwise wouldn’t have made without the help of AI. I wouldn’t bother with transcripts if I had to do them manually, so now the interview is more accessible to audiences who prefer to read instead of listen or watch the interview. It’s also more discoverable, and a better overall experience for everybody. Hope this long-form, detailed post is useful to those learning to use AI tools. I'm continuing to make this process faster each time. Would appreciate any of your AI tips if you have them!

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

If you follow me here, you probably know I run a podcast about AI—What the AI?! But did you know I use AI to help run the podcast itself? Here’s how I’ve built my workflow using GumLoop, which has been a game-changer for automating multi-step AI and non-AI tasks into a seamless process. 🎙 Step 1: Generating the RundownAnnie and I record from a structured doc we call The Rundown—a document that outlines: 📌 The stories we’ll cover 📝 Key talking points 🎯 Hooks and intros Each week, I maintain a spreadsheet of URLs for stories I want to discuss. The day before recording, I kick off my GumLoop flow, which: 🔍 Scrapes the article from each URL 🧠 Uses the Perplexity API to add relevant details and context, plus sources 🤖 Passes everything to ChatGPT, which summarizes key points, organizes them, and crafts a strong hook and intro 🔗 Ensures the original sources remain intact (no AI hallucinations here!) 📄 Writes the final output into a Google Doc I don’t use this version as-is—I tweak, combine, and refine—but it saves me a huge amount of time in prepping each week. 🚀 This is just one of the ways I’m using AI to streamline my work. Step 2 (the post-show-flow) coming tomorrow! How are you using AI or automation tools in your workflow? Drop your thoughts in the comments! #AI #Podcasting #Automation #GumLoop #Perplexity

Here are the BEST AI tools for podcasting 🎧 I've been sharing quite a lot about AI recently, and where I genuinely think it's helped me the most is in small teams I run, like our tiny but mighty podcast team. Podcasting can seem like a big investment, but AI (and sharing resources) can hugely lower the barriers to entry & make it much easier to get up & running. We've spent months now looking for the best AI tools that can save us time, so we can spend it experimenting and trying cooler things, and these are the tools we've found that really work for us. If you want to get into podcasting, give them a try! No. 1, Auphonic. We moved the podcast from a rather dingy studio into my new office this year - it looks incredible (if I do say so myself), but we are right next to a train line 😬 so we set out on a mission to find a tool that would get rid of the background noise. Auphonic uses AI to balance the audio levels, reduce noise, and optimize quality. It’s saved us countless hours in editing and thousands on soundproofing. No 2. Riverside.fm. It's known for remote recordings (which we very rarely do for WH,HW), but I've found their AI transcription & show notes tools to be really brilliant. It automatically picks out the main themes of the conversation, which helps when we're drafting the narratives for our trailers too. I'm yet to try their AI voice feature though, maybe because I'm scared it'll be better at hosting the podcast than me. No 3 is a bit of a cheat as there's not a huge amount of AI in it, but it's Frame.io. We use Frame for all our file storage & reviews. The interface is really beautiful (I love tech that works as beautifully as it looks), and it's so easy to feedback on specific moments and assign files to members of the team. I'm always looking for more recommendations so if you have any, please leave them in the comments!