Building RAG Pipelines with LLMs

• Chapter 2.1: Dense vs. Sparse Retrieval

• Chapter 2.2: Vector Embeddings and Semantic Search

• Chapter 2.3: Overview of Tools (FAISS, Weaviate, Pinecone, Qdrant)

• Chapter 3.1: Embedding Generation (OpenAI, Hugging Face)

• Chapter 3.2: Chunking and Preprocessing Strategies

• Chapter 3.3: Prompt Templates for RAG

• Chapter 3.4: Connecting LLMs to Vector DBs

• Chapter 4.1: Document Ingestion and Indexing

• Chapter 4.2: Query Handling and Retrieval Flow

• Chapter 4.3: Response Synthesis using LLMs

• Chapter 4.4: Evaluation Metrics for RAG Responses

• Chapter 5.1: Hybrid Search (BM25 + Embeddings)

• Chapter 5.2: RAG with Structured and Unstructured Data

• Chapter 5.3: Multi-turn and Conversational RAG

• Chapter 6.1: Deploying RAG Systems with LangChain or LlamaIndex

• Chapter 6.2: Monitoring, Caching, and API Design

• Chapter 6.3: Capstone Project – Build Your Own RAG Pipeline

• Chapter 6.4: Industry Use Cases and Future Trends

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