If you’re like me, you probably love listening to podcasts while doing something else—whether it’s driving, exercising, or just relaxing. But the problem with podcasts, compared to other forms of media like books or articles, is that they don’t naturally lend themselves to note-taking. How often have you heard an insightful segment only to realize, days or weeks later, that you can’t remember which podcast it was from, let alone the details?

This has been my recurring issue: I’ll hear something that sparks my interest or makes me think, but I can’t for the life of me figure out where I heard it. Was it an episode of Hidden Brain? Or maybe Freakonomics? By the time I sit down to find it, the content feels like a needle lost in a haystack of audio files. Not to mention the fact that my podcast player deletes episodes after I listen to them and I’m often weeks or months behind on some podcasts.

This is exactly where the concept of Retrieval-Augmented Generation (RAG) comes in. Imagine having a personal assistant that could sift through all those hours of podcast content, pull out the exact episode, and give you the precise snippet that you need. No more digging, scrubbing through audio files, or guessing—just a clear, searchable interface that makes those moments instantly accessible.

In this post, I’m going to walk you through how I set up my own RAG system for podcasts—a system that makes it possible to recall insights from my podcast archive just by asking a question. Whether you’re new to AI or just interested in making your podcasts more actionable, this guide will take you step-by-step through the process of turning audio into accessible knowledge.

Introducing Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) acts as a bridge between the stored data and a language model. It allows you to search for specific information and generates detailed, context-rich responses based on that data. Imagine asking, “What was that podcast that talked about the evolution of money?”—instead of spending hours searching, RAG can pull the relevant snippet and give you an insightful answer.

By connecting the steps I’ve covered in previous posts—downloading, organizing, transcribing, and embedding—you’ll be able to transform your podcast library into a powerful, searchable tool. Let’s dive into how we can achieve that by using RAG.

Setting Up the Podcast RAG System

For those interested in the full setup details and code, I’ve built a prototype of my RAG system, which you can check out in the repository: Podcast RAG Prototype.

To show the power of this system, I’ve prepared two demonstrations—one using the Gemma model and another using Gemini. These demos illustrate how effectively the RAG system can retrieve podcast insights.

In both instances, I used a simple query:

python3 src/rag.py --query "The rise of artificial intelligence"

I also used a prompt template that looked like this:

Instructions:
You are a helpful research assistant. Use the context provided to answer the question.
Context:
----------------------------------------
Podcast Name: $podcast
Episode Name: $episode
Content: $transcript
----------------------------------------
Question: 
What does my archive contain about $query
Answer:

This prompt template contained the top 10 responses from my vector database, with their relevant transcript data. I did not force a single response per podcast, or do any other post-processing on the vector search results.

I experimented quite a bit with more detailed prompts and even included some few-shot examples at one point, but found that in this case both models did better with a simpler prompt.

Gemma

In the Gemma case, I was using gemma2:27b, and I set the Ollama context window to 8192 to allow the model to consider its maximum context window. My prompt was still longer than that, and as a result, the model loses some of the data from the higher-ranked search results. That leads me to think that I’ll need to adjust my search strategy to get better results with Gemma. I also had a hard time getting this model to reliably cite the podcast episodes regardless of the prompt techniques I used.

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Your archive contains information about the potential dangers and ethical considerations surrounding the rise of artificial intelligence (AI). It explores these key points:

• The Potential for Superintelligence: Your archive highlights concerns from experts like Sam Harris about the possibility of AI surpassing human intelligence, leading to an “intelligence explosion” where AI rapidly improves itself beyond our control. It uses the analogy of ants and humans to illustrate how a superior intelligence might treat us with disregard if our goals diverge.
• The Nature of Intelligence: It discusses the philosophical debate around what constitutes intelligence and whether it’s solely tied to reason or encompasses other factors. The archive references Plato’s idea of the philosopher-king, where the most rational individual should lead society. However, it also critiques this notion as potentially leading to harmful hierarchies based on perceived intellectual superiority.
• The Importance of Ethical Development: Your archive emphasizes the need for careful and ethical development of AI. It suggests that simply assuming progress will be beneficial is insufficient. Instead, we need open discussions about potential risks and safeguards to ensure AI benefits humanity.

Key Takeaways:

• The rise of AI presents both exciting opportunities and significant challenges.
• We must prioritize ethical considerations in AI development to prevent unintended consequences.
• Ongoing dialogue and critical thinking are crucial for navigating the complexities of AI’s impact on society.

Let me know if you have any other questions about the content in your archive!

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Gemini

In the case of Gemini, I was using gemini-1.5-flash for my model. This system has a huge input context length, and it made a big difference in the results. The model reliably cites the podcast episodes and does a great job at organizing the results when there were multiple entries for a single episode.

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Your archive contains several podcasts that discuss the rise of artificial intelligence (AI) and its potential consequences. Here are some key points from these podcasts:

Philosophize This – Episode 184: Is Artificial Intelligence Really an Existential Risk?

• Definition of Intelligence: The episode explores different definitions of intelligence, from narrow intelligence (e.g., a chess computer) to general intelligence (e.g., human-like intelligence).
• ChatGPT as an Example: The episode uses ChatGPT as an example of narrow intelligence, suggesting that its abilities may be a stepping stone towards general intelligence.
• General Intelligence as a New Species: The episode highlights the potential for AI to develop general intelligence and become a new species, distinct from humans.
• Existential Risk: The episode discusses the potential existential risk posed by AI, particularly the lack of understanding of how human intelligence operates and the unpredictable nature of general intelligence.

TED Radio Hour – Episode: Future Consequences

• Intelligence Explosion: The episode explores the concept of an “intelligence explosion,” where AI rapidly surpasses human intelligence and becomes self-improving.
• Platform-Independent Intelligence: The episode argues that intelligence is not tied to biological substrates, suggesting that AI can surpass human intelligence.
• Divergence of Goals: The episode expresses concern that even if AI is not intentionally malicious, its goals may diverge from human goals, potentially leading to harm.
• Need for Urgent Action: The episode emphasizes the need for urgent action to address the potential risks of AI, comparing the situation to the arrival of an alien civilization.

Other Relevant Content

• Philosophize This – Episode 186: Are We Heading for a Digital Prison – Panopticon (Foucault, Bentham, Cave). This episode explores the potential for AI to create a digital panopticon, where our behavior is constantly monitored and controlled.
• Stuff You Should Know – Episode: What Makes a Genius. This episode discusses the nature of intelligence and genius, exploring how these concepts are often measured and the limitations of such measurements.

Overall

Your archive provides a rich overview of the current debate surrounding the rise of AI, covering its potential benefits, risks, and philosophical implications. It explores the potential for AI to surpass human intelligence, the importance of understanding AI’s goals, and the need for ethical considerations in AI development.

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Conclusion: Reflections and Next Steps

The journey of building a Retrieval-Augmented Generation (RAG) system for podcasts has been both challenging and rewarding. Through this exploration, I’ve found ways to transform a passive collection of podcast content into an actionable and searchable knowledge base, turning fleeting moments of insight into something that can be easily recalled and used. The use of both the Gemma and Gemini models highlights the potential of RAG to bring real value, providing nuanced and context-rich responses from complex archives.

While there are still some technical hurdles, such as improving search strategies and prompt effectiveness, the results so far are promising. This system has already begun to solve a real problem: giving us the ability to recall and utilize knowledge that would otherwise be lost in hours of audio recordings.

If you’re interested in creating a similar system or expanding on what I’ve done, I encourage you to dive into the prototype and explore how RAG can be applied to your own datasets. Whether you’re working with podcasts, documents, or any other unstructured content, the potential for making that content more accessible and useful is vast.

Moving forward, I’ll continue refining the RAG system and experimenting with different models and configurations. If you have any questions, suggestions, or would like to share your own experiments, feel free to reach out.

Thank you for following along on this journey—let’s continue exploring the power of AI together.