All about #RAG - Retrieval Augmented Generation

This video explains Retrieval-Augmented Generation (RAG), a powerful technique that enhances large language models by enabling them to fetch relevant external data before generating responses. It covers how RAG combines document retrieval and natural language generation, and outlines practical ways to implement RAG using frameworks like LangChain, LlamaIndex, and Haystack. Viewers learn about chunking, embedding, vector databases, and building RAG pipelines. Real-world use cases—like knowledge bots, legal assistants, and support chatbots—are explored. --------------- Timestamps 0:00 - Intro 00:20 - What is RAG? 01:20 - Why RAG? 01:29 - RAG Architecture 02:12 - Tools to implement RAG 03:02 - Practical use cases of RAG 03:46 - Challenges with RAG 05:59 - RAG in summary 06:12 - Outro --------------- #generativeai #learnai #LLMs #RAG --------------- Complete AI Learning Playlist: https://www.youtube.com/playlist?list=PL9iLtz3CXQMuXYz8e1uirPsau7rZNIXMw Follow on LinkedIn: https://www.linkedin.com/in/gauravbehere/ --------------- Buy me a coffee: https://www.patreon.com/coderash https://www.paypal.com/paypalme/gauravbehere --------------- Search keywords: rag,rag ai,retrieval augmented generation,rag framework,rag explained,what is rag ai,how rag works,rag langchain,rag tutorial,rag in ai,rag with openai,rag with llama3,rag use cases,rag implementation,rag architecture,rag demo,build rag app,rag python tutorial,rag with langchain and openai,rag step by step,rag vs fine tuning,rag vs training,rag search engine,rag llm,rag concept,rag use in enterprise ai,rag for business,rag vs chatgpt,rag with faiss,rag with pinecone,rag with chromadb,rag vs vector search,rag vs traditional nlp,rag vs question answering,rag indexing,rag chunking,rag embedding,rag retrieval,rag generation,rag prompt engineering,rag prompt design,rag agent,rag chatbot,build chatbot with rag,rag in production,open source rag,rag with gpt4,rag with gpt3.5,rag with claude,rag with mistral,rag with huggingface,rag for legal,rag for medical,rag for research,rag for support bots,rag eval,rag evaluation,rag performance,rag with llamaindex,rag vs llamaindex,rag vs haystack,rag vs langchain,rag local,rag offline,rag ollama,ollama rag tutorial,rag for developers,ai with rag,rag ai pipeline,rag for document search,rag question answering,rag with pdf,rag with html docs,rag in enterprise,rag knowledge base,rag knowledge bot,rag vs retriever,rag detailed explanation,rag code example,rag for beginners,rag for experts,rag for ai engineers,rag how to,rag implementation tutorial,rag youtube tutorial,langchain rag example,build ai assistant with rag,create rag chatbot,rag integration,rag demo app,rag for startups,rag for saas,rag deployment,rag inference,rag llm orchestration,rag api integration,rag vs finetune,rag vs gpt agents,rag vs context injection,rag vs document qa,rag question answering system,rag semantic search,rag based retrieval,rag hybrid search,rag vs similarity search,rag for knowledge retrieval,rag for product faq,rag summarizer,rag legal bot,rag healthcare assistant,rag smart bot,rag transformer,rag ragas evaluation,rag metrics,rag performance tuning,rag vs retriever generator,rag design pattern,rag software architecture,rag video tutorial,rag youtube seo,rag educational content,rag course,rag deep dive,rag 2025 tutorial,rag new frameworks,rag current state,rag vs neural search,rag chatbot langchain,rag vector db,rag document loader,rag embeddings openai,rag embeddings huggingface,rag embeddings azure,rag embeddings bge,rag embeddings all-mpnet,rag with sentence-transformers,rag realtime,rag async retrieval,rag caching,rag fastapi,rag streamlit,rag frontend,rag ux design,rag conversational ai,rag copilot,rag search based chatbot,rag dynamic context,rag for knowledge workers,rag architecture overview,rag practical guide,rag ai bot tutorial,rag beginner friendly,rag pipeline langchain,rag vs rag chain,rag agentic rag,rag memory,rag conversational memory,rag for private data,rag with local llm,rag setup,rag chatbot from scratch,rag integration with chatgpt,rag for chat ui,rag backend setup,rag frontend integration,rag design guide,rag api,rag api call example,rag chunk overlap,rag best practices,rag open source tool,rag vs retriever augmented,rag grounding,rag hallucination mitigation,rag response accuracy,rag summary generation,rag question answer system,rag with gpt,rag with llama,rag with mistral 7b,rag security,rag for banking,rag for finance,rag for knowledge graph,rag hybrid architecture,rag performance issues,rag with unstructured data,rag with sql,rag vs traditional search,rag query understanding,rag chat interface,rag and semantic search,rag vector db comparison,rag framework comparison,rag architecture tutorial,rag high level overview,rag backend tutorial,rag for developers 2025,rag ai course free,rag online tutorial,rag vs neural retriever,rag data prep,rag pipelines overview,rag how it works,rag for ai startups,rag and langchain integration