Module 20: Text Chunking & Embedding Techniques
- Understanding document splitting
- Hierarchical & semantic chunking
- Embedding types & use cases
- Retrieval strategies
Module 21: Retrieval-Augmented Generation (RAG)
- Why RAG is essential for accuracy
- Building complete RAG pipelines
- Query compression & reranking
- Graph-based RAG
- Enterprise-ready RAG workflows
Module 22: LangChain Developer Track
- Core building blocks (LLMs, Chains, Tools, Memory)
- Creating agents with LangChain
- Multi-agent systems (CrewAI, AutoGen)
- Implementing tool calling
- Building an intelligent assistant app
Module 23: Vector Databases
- How vector databases work
- ChromaDB
- FAISS
- Pinecone
- Weaviate
- Qdrant
- Storing & retrieving embeddings