Is RAG Still Needed? Choosing the Best Approach for LLMs

📘 Free AI Prompt Engineering Guide (HubSpot): https://clickhubspot.com/096149 RAG (Retrieval-Augmented Generation) is one of the most important concepts in AI engineering but building a production-ready RAG system is much more than embedding documents into a vector database. In this video, I explain how production RAG systems actually work, why basic RAG pipelines fail, and the techniques AI engineers use to build accurate, scalable, and reliable retrieval systems. 💡 Here's what you'll learn: ✅ What RAG (Retrieval-Augmented Generation) is and why it's used ✅ How the RAG pipeline works: Indexing → Retrieval → Generation ✅ Embeddings, vector databases, and semantic search explained ✅ Why naive RAG systems fail in production ✅ How chunking strategy affects retrieval quality ✅ Hybrid Search (Vector Search + BM25) for better accuracy ✅ Reranking to improve retrieval relevance ✅ Query Transformation and HyDE for smarter search ✅ Agentic RAG and how it enables multi-step reasoning ✅ When to use RAG instead of fine-tuning ✅ Best practices for building production-ready AI applications 👋 about me I’m Maddy, a senior software engineer (prev. at Google), with prior internships at Microsoft, Morgan Stanley, IBM, and Amazon. Sharing my journey here - thanks for watching 🤍 🔗find me on other socials Instagram   / madeline.m.zhang   LinkedIn   / madelinemzhang   Tiktok   / madeline.m.zhang   📖 Timestamps 0:00 Intro 0:47 Why RAG Matters 1:22 RAG vs Fine-Tuning 2:50 How the RAG Pipeline Works 4:29 Why Basic RAG Fails 6:32 Production RAG Techniques 9:06 Agentic RAG Explained 10:15 Why RAG Matters for AI Engineers 10:55 Recap 🔔 Subscribe for more software engineering, AI tools, coding, and tech career videos! disclaimer: views are all my own and do not represent any current / past employer(s) Thank you to HubSpot for sponsoring this video. #rag #retrievalaugmentedgeneration #ai #artificialintelligence #llm #llmengineering #aiengineering #softwareengineering #softwareengineer #generativeai #vectorsearch #vectordatabase #embeddings #semanticsearch #bm25 #hybridsearch #agenticai #agenticrag #systemdesign #machinelearning #openai #anthropic #claude #chatgpt #programming #coding #developer #techcareers #aitools #contextengineering