RAG systems have revolutionized information retrieval, but a hidden pitfall lurks: confusing similarity with relevance. In this livestream, we'll delve into the nuances of these concepts and explore how they impact RAG performance.

RAG Fundamentals: We'll start with a basic RAG system breakdown, illustrating how it retrieves text chunks based on keyword matches.

Defining Relevance: Discover the importance of relevance in RAG, understanding it as the likelihood of a text chunk directly answering your query.

The Power (and Limitations) of Similarity: We'll explore semantic search, a powerful retrieval strategy that uses context to find relevant information. However, you'll also learn when similarity can lead RAG astray.

Real-World Examples: Dive into scenarios where similarity fails to meet our information needs. We'll see how seemingly relevant text might not answer your specific question.

Mitigating the Problem: Uncover strategies to improve RAG's accuracy. We'll discuss techniques like sub-query generation, entity recognition, and advanced retrieval models.

The Bigger Picture: Explore how RAG integrates with content-based retrieval and hybrid search approaches to refine results

Beyond Basic RAG