Скачать или смотреть RAG Series Part 3 - SOTA RAG Retrieval: BM25, Reranking, & Query Expansion Explained

Part 3 is the deep dive into the true art of RAG: Retrieval.

GitHub- https://github.com/kamalkrbh/How-to-I...

Basic vector search is often unreliable. In this video, we move beyond simple semantic search by combining advanced techniques like BM25 for keyword matching, LLM-based Query Expansion to cover synonyms, and Neural Network Reranking (Cross-Encoders) to guarantee the most relevant chunk is always returned.

Learn the essential strategies needed to build a State-of-the-Art (SOTA) RAG system.

Chapters-
00:00:00 Introduction & Recap
00:00:41 Retrieval Overview: Why it Matters
00:04:05 Dense Retrieval (Vector Search) Explained
00:09:54 Hybrid Retrieval: Combining Dense Search with BM25
00:10:02 What is BM25? (Term Frequency, IDF, Diminishing Returns)
00:18:48 Why You Should Combine Vector Search and BM25
00:29:10 Query Expansion (Handling Poor User Queries)
00:30:42 LLM-Based Query Expansion Techniques (Synonyms & Rewriting)
00:37:13 Reranking: Advanced Relevance Scoring
00:37:41 Cross Encoder Reranking (Neural Network Approach)
00:45:57 Context Filtering (Similarity Threshold, Metadata, Intent)
00:50:32 Conclusion & Preview of Part 4: Augmentation/Generation

RAG Series Course Navigation:
Part 1: RAG is 90% Retrieval: The Part Most Developers Miss
👉    • RAG Series Part 1 - RAG is 90% Retrieval: ...  

Part 2: The RAG Secret: How to Create PERFECT Chunks for 10x Accuracy
👉    • RAG Series Part 2 - The RAG Secret: How to...  

▶️ *Part 3 (Current Video): SOTA RAG Retrieval: BM25, Reranking, & Query Expansion*

Part 4: Augmentation, Context Window Management, and Prompt Templates
👉    • RAG Series Part 4 - Prompt Engineering, Co...  

#RAG #BM25 #HybridSearch #Reranking #QueryExpansion #LLMOps #AIEngineering