RAG is Broken? Here’s Why We Need Corrective RAG (CRAG) скачать в хорошем качестве

RAG is Broken? Here’s Why We Need Corrective RAG (CRAG)

Corrective RAG (CRAG) Explained in Detail 🚀 In this video: • What is Retrieval-Augmented Generation (RAG)? • How traditional RAG works • The major problems with RAG • Why RAG sometimes fails • What is Corrective RAG (CRAG)? • How CRAG improves retrieval quality • Why we need CRAG for reliable LLM applications Traditional RAG systems often struggle with poor retrieval quality, irrelevant documents, hallucinations, and noisy context. That’s where Corrective RAG (CRAG) comes in. CRAG introduces a correction mechanism that evaluates and improves retrieved documents before passing them to the language model, making AI systems more accurate, reliable, and production-ready. If you're building LLM applications, chatbots, AI agents, or working on GenAI systems, understanding CRAG is essential. Paper Link : https://arxiv.org/pdf/2401.15884 LinkedIn :   / deepak-joshi-software-engineer   🔎 Keywords Covered Corrective RAG CRAG architecture Retrieval-Augmented Generation RAG problems RAG vs CRAG LLM hallucination Advanced RAG techniques GenAI architecture AI retrieval systems LLM production systems 💡 Who is this for? AI Engineers Machine Learning Engineers LLM Developers GenAI Builders Data Scientists Anyone learning Advanced RAG