LLM & RAG - Learn the Game Changing Techniques

Welcome back to the channel. I'm Gaurav, and today we're diving deep into, two, game-changing technologies that are literally reshaping how we interact with AI: Large Language Models, or LLMs, and Retrieval-Augmented Generation, better known as RAG. If you've ever wondered how ChatGPT actually works under the hood, or why some AI assistants seem to know incredibly specific information about your company's internal documents, this video is for you. We're going to break down these complex systems in a way that actually makes sense. If you are new to AI & wondering where to start, this could be a good starting point #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: how large language models work,LLM explained,what is a large language model,rag explained,how does RAG work,rag ai architecture,rag machine learning,rag NLP,rag and LLM explained,rag vs langchain,rag vs vector search,rag vs transformers,rag vs semantic search,how GPT works,openAI model explanation,rag model explained,rag framework,LLM NLP tutorial,LLM vs transformer,rag for QA,how to use RAG with LLM,how large language models understand language,LLM and knowledge retrieval,LLM and embedding,LLM memory explained,how AI retrieves information,rag indexing,rag vector search,faiss vector search,rag pinecone,open source rag tool,haystack rag,how retrieval works in AI,how to combine search with LLM,LLM architecture simplified,rag diagram explained,LLM in layman terms,rag natural language processing,how LLMs think,LLM training phases,rag model use cases,rag vs fine tuning,rag with GPT,rag with BERT,rag with LLaMA3,rag with Ollama,rag chaining explained,how chatbot remembers,rag for developers,LLM inference,rag vs search engine,rag prompt engineering,embedding and search,rag step by step,rag faiss example,rag semantic chunking,rag deep dive,LLM search ranking,vector index with LLM,rag vs closed book QA,rag context injection,LLM grounding,rag grounded generation,LLM factual response,rag knowledge base,rag document question answering,rag LangChain integration,langchain rag architecture,rag and langchain tutorial,rag vs knowledge graph,LLM with database,rag hybrid search,rag for enterprise,how AI search works,rag for customer support,LLM augmented retrieval,rag training pipeline,rag with sentence transformers,how AI generates answers,LLM vs human intelligence,rag content generation,rag research paper summary,rag vs zero-shot,rag vs few-shot,rag chain of thought,rag scalability,rag memory optimization,rag for LLM accuracy,rag chatbot tutorial,LLM retrieval tutorial,embedding vs tokenization,LLM with huggingface,LLM indexing best practices,rag knowledge distillation,LLM hallucination prevention,rag grounded answers,rag architecture deep dive,how to build LLM with RAG,rag vs open domain QA,rag relevance scoring,LLM tuning vs rag,rag for fact-based answers,rag for summarization,rag vs prompt tuning,LLM facts retrieval,rag multi-hop QA,rag vs wikipedia search,rag vs embedding lookup,rag and neural retrievers,rag for medical AI,rag in finance industry,rag for enterprise search,rag security and privacy,rag prompt templates,rag and metadata filtering,LLM vector store,rag chunking strategies,rag knowledge injection,rag in autonomous agents,rag use in GPT agents,rag vs classic retrieval,rag streaming response,rag vs conversational memory,LLM document retriever,rag personalized answers,LLM based customer service,rag performance tuning,rag latency reduction,rag deep learning use case,LLM transformer inner workings,rag for coding copilots,rag use with coding LLMs,rag developer tools,rag with local models,rag pipeline setup,LLM vs knowledge graphs,rag in recommendation systems,rag for technical support,LLM data grounding,rag architecture with Redis,how retrieval works in AI models,how AI uses memory,how does AI remember context,context windows in LLM,rag embeddings tutorial,semantic chunking best practices,how to setup a rag pipeline,rag index creation,rag langchain walkthrough,rag chaining models,rag quality metrics,rag evaluation techniques,rag prompt context window,how AI reads documents,LLM indexing architecture,vector store in rag,rag vs transformer memory,rag vs external tools,rag database integration,rag optimization techniques,rag for accuracy improvement,rag and chatbot memory,LLM dynamic context injection,rag for legal document search,rag content QA flow,rag for summarizing large texts,rag and factual grounding,rag implementation best practices,rag for internal knowledge bases,LLM accuracy challenges,LLM with external knowledge,rag scaling issues,rag with llamaindex,rag for enterprise applications