RAG Applications with Vector Databases

RAG Applications with Vector Databases

Focused training on data chunking, embedding pipelines, vector stores, retrieval quality, and grounded response generation for enterprise assistant use cases.

What you'll learn, module by module

Updated June 2026

1. 1

   Module 1: RAG Architecture Fundamentals - when and why retrieval beats fine-tuning in 2026

2. 2

   Module 2: Data Chunking Strategies - semantic, recursive, and token-aware chunking

3. 3

   Module 3: Embeddings Deep Dive - model selection and embedding quality evaluation

4. 4

   Module 4: Vector Databases - FAISS, Chroma, and managed vector store options

5. 5

   Module 5: Retrieval Quality - hybrid search, re-ranking, and relevance tuning

6. 6

   Module 6: Grounded Generation - citation, source attribution, and reducing hallucination

7. 7

   Module 7: Latency and Cost Optimization for production RAG pipelines

8. 8

   Module 8: Capstone - build a policy-aware knowledge base assistant