Generative AI from Basic to Advance - Advance RAG Course: Master All RAG Retrieval & Reranking Techniques in One Video💡!

RAG systems combine the power of retrieval mechanisms with generative models to create more informed and contextually accurate responses. In this Advanced RAG Tutorial, we cover **every retriever and reranker method** used in modern RAG pipelines: 🔸 Vector Store (Chroma, Weviate, Faiss) 🔸 BM25 / Sparse Retrieval 🔸 Self-Query Retriever, Parent Doc Retriever, Sentence Window 🔸 Reranking Models (Cohere, BAAI, ReRanker, CrossEncoder) If you're building a custom chatbot, QA system, or AI assistant—this is your one-stop guide! 💥 📌 Best for: Developers, ML Engineers, LLM enthusiasts Don't miss out; learn with me! 📢 Like 👍 | Comment 💬 | Subscribe 🔔 for more in-depth LLM content! #llm #embedding #ai #futureai #generativeai #genai #textgeneration #ragapp #langchain #programminglogic #python #chatbot #openai #gpt #langchainj #rag #reranking #cohereai #bm25 #crossencoder #transformers #multiretriever #ragfusion #advancerag #llamaindex #RAGTutorial #AdvanceRAG #Retriever #Reranker #LangChain #LLMApplications #RAGStack #RAGPipeline #VectorSearch #semanticsearch #CohereReranker #MMR #HybridSearch Complete GenAI Material: https://github.com/sunnysavita10/Generative-AI-Indepth-Basic-to-Advance Connect with me on Social Media- LinkedIn : https://www.linkedin.com/in/sunny-savita/ One to One Call: https://topmate.io/sunny_savita10 GitHub : https://github.com/sunnysavita10 Telegram : https://t.me/aimldlds 00:00:00 Introduction Overview of the course, prerequisites, and what to expect. 00:05:00 RAG Fundamentals Recap What is RAG? Basic RAG architecture and workflow. 00:15:00 Data Preparation Loading and chunking documents. Preprocessing and cleaning text. 00:30:00 Sparse Retrieval Techniques Keyword search (TF-IDF, BM25). Implementing basic retrievers. 01:00:00 Dense Retrieval Techniques Embeddings and vector search. Using open-source models for dense retrieval. 01:30:00 Hybrid Retrieval Combining sparse and dense retrievers. Weighted ensemble techniques. 02:00:00 Advanced Retriever Types Self-query retriever, Contextual compression retriever, Parent document retriever, Sentence window retriever, Auto-merging retriever. 03:00:00 Reranking Techniques Why reranking is important. Using cross-encoders and other models for reranking. 03:45:00 Integrating Retrieval with LLMs Passing retrieved results to LLMs. Prompt engineering for RAG. 04:30:00 Real-World Applications Case studies and practical projects. Building a RAG-powered chatbot. 05:30:00 Scaling and Optimization Performance tips. Using GPUs and quantized models. 06:15:00 Open-Source Tools and Libraries LangChain, ChromaDB, and other ecosystem tools. Setting up and configuring retrieval pipelines. 07:00:00 Troubleshooting and FAQs Common issues and their solutions. Best practices. 07:45:00 Final Project Walkthrough End-to-end implementation. Review and wrap-up. 08:00:00 Conclusion and Next Steps Where to go from here. Further resources and advanced topics.