The RAG Reality Check: When Retrieval-Augmented Generation Works (and When It Fails)

Description

Retrieval-Augmented Generation (RAG) is often sold as a fix for hallucinations and outdated knowledge in large language models. In practice, it’s neither a silver bullet nor a default architecture. In this video, we break down what RAG actually does, when it works reliably, and the failure modes that catch teams off guard in production systems. You’ll learn: • Why RAG reduces some hallucinations but introduces new ones • How retrieval based on semantic similarity can lead to confident but wrong answers • The types of tasks where RAG performs well — and where it fails badly • The hidden costs: latency, complexity, and evaluation challenges This is not a vendor comparison or a step-by-step tutorial. It’s a practical, system-level reality check for engineers, tech leads, and product teams building real AI workflows. Key takeaway: RAG doesn’t make models smarter — it gives them better notes, sometimes. — Practical AI Lab Clear, practical explanations of AI concepts for real-world work.