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
