In this episode, we explore how large language models (LLMs) have human-computer interaction and revolutionized why they're not without limitations.

While LLMs can generate impressively human-like responses, they often rely on static training data, leading to outdated or inaccurate answers that may erode user trust.

To address these challenges, we dive into the powerful technique of Retrieval-Augmented Generation (RAG).

Learn how RAG enhances LLMs by combining their generative abilities with real-time, reliable data sources—resulting in more accurate, up-to-date, and trustworthy AI outputs.

We break down:

- How Retrieval-Augmented Generation works

- Why semantic search is critical in this process

- The cost and control advantages of RAG for enterprises

- Best practices for implementing RAG in real-world systems

Whether you’re an AI developer, tech leader, or simply curious about the future of generative AI, this episode gives you the tools to understand how to make AI work smarter, not harder.