Why Your Retriever is Failing and How Context Can Save It Imagine asking “I want to buy apple” – do you mean Apple Inc. stock, the latest iPhone, or simply fruit? Without context, your retriever may serve you the wrong results. 1. What Is the Problem in Your Retriever & Embedding? Modern retrievers map queries and documents into high-dimensional vectors (embeddings) and rank by cosine similarity. But when a query is ambiguous, plain embeddings struggle:...
Improving Retrieval in RAG (via Recall, Precision, and NDCG) Introduction Retrieval-Augmented Generation (RAG) is the superhero sidekick that grounds your Large Language Model (LLM) in cold, hard facts. But here’s the dirty secret: if your retrieval sucks, your RAG system is just a fancy chatbot with a broken brain. Weak retrieval = missed documents, irrelevant results, and rankings that make no sense. This guide cuts through the noise. You’ll learn how to turbocharge your RAG retrieval with a no-fluff, step-by-step approach to maximize recall, sharpen precision, and nail NDCG....