In this episode, we break down Retrieval-Augmented Generation (RAG)—the architecture that's enabling AI systems to tap into your company's private data in real time. Drawing from the first three chapters of the second edition of Keith Bourne's Unlocking Data with Generative AI and RAG, we explore what RAG is, why it's become essential now, and how it compares to alternatives like fine-tuning.

What We Cover

• The RAG promise: Giving AI access to your proprietary documents, customer histories, and internal knowledge—not just public training data
• How it works: The three-step process of indexing, retrieval, and generation that keeps your AI current without costly retraining
• Why now: The convergence of massive context windows (up to 10M tokens), mature tooling like LangChain (70M+ monthly downloads), and scalable infrastructure
• RAG vs. fine-tuning: When to use each approach, and why the smartest teams combine both
• Real-world applications: Customer support, wealth management, healthcare, e-commerce, and internal knowledge bases
• Honest limitations: Data quality dependencies, pipeline complexity, latency trade-offs, and the persistent challenge of hallucinations

Key Tools Mentioned

LangChain, LlamaIndex, Chroma DB, OpenAI Embeddings, Meta Llama, Google Gemini, Anthropic Claude, NumPy, Beautiful Soup

Resources

For detailed diagrams, thorough explanations, and hands-on code labs, grab the second edition of Unlocking Data with Generative AI and RAG by Keith Bourne—available on Amazon.

Find Keith Bourne on LinkedIn.

Produced by Memriq | memriq.ai