Advanced RAG

- Intro to RAG and Why LLMs Need External Knowledge - LLM Limitations and How Retrieval Fixes Hallucinations - How RAG Combines Search + Generation Into One System - Fresh Data Retrieval to Overcome Frozen Training Cutoffs - Context Engineering for Giving LLMs the Right Evidence - Multi-Agent RAG and Routing Queries to the Right Tools - Retrieval Indexes: Vector DBs, APIs, SQL, and Web Search - Query Routing With Prompts and Model-Driven Decision Logic - API Calls vs RAG: When You Need Data vs Full Answers - Tool Calling for Weather, Stocks, Databases, and More - Chunking Long Documents Into Searchable Units - Chunk Size Trade-offs for Precision vs Broad Context - Metadata Extraction to Link Related Chunks Together - Semantic Search Using Embeddings for Nearest-Neighbor Retrieval - Image and Multimodal Handling for RAG Pipelines - Text-Based Image Descriptions vs True Image Embeddings - Query Rewriting for Broad, Vague, or Ambiguous Questions - Hybrid Retrieval Using Metadata + Embeddings Together - Rerankers to Push the Correct Chunk to the Top - Vector Databases and How They Index Embeddings at Scale - Term-Based vs Embedding-Based vs Hybrid Search - Multi-Vector RAG and When to Use Multiple Embedding Models - Retrieval Indexes Beyond Vector DBs: APIs, SQL, Search Engines - Generation Stage: Stitching Evidence Into Final Answers - Tool Calling With Multiple Retrieval Sources for Complex Tasks - Synthetic Data for Stress-Testing Retrieval Quality Early - RAG vs Fine-Tuning: When to Retrieve and When to Update the Model - Prompt Patterns for Retrieval-Driven Generation - Evaluating Retrieval: Recall, Relevance, and Chunk Quality - Building End-to-End RAG Systems for Real Applications