Build retrieval-augmented applications using embeddings and vector search.
Focused training on data chunking, embedding pipelines, vector stores, retrieval quality, and grounded response generation for enterprise assistant use cases.
Updated June 2026
Module 1: RAG Architecture Fundamentals - when and why retrieval beats fine-tuning in 2026
Module 2: Data Chunking Strategies - semantic, recursive, and token-aware chunking
Module 3: Embeddings Deep Dive - model selection and embedding quality evaluation
Module 4: Vector Databases - FAISS, Chroma, and managed vector store options
Module 5: Retrieval Quality - hybrid search, re-ranking, and relevance tuning
Module 6: Grounded Generation - citation, source attribution, and reducing hallucination
Module 7: Latency and Cost Optimization for production RAG pipelines
Module 8: Capstone - build a policy-aware knowledge base assistant
RAG architecture
Embedding strategy
Retrieval evaluation
Latency optimization
Knowledge base assistant
Policy-aware retrieval bot
Is this course suitable for working professionals?
Yes. The course includes flexible recorded support and assignment windows for working learners.
Do I get certification preparation support?
Yes. This program includes structured guidance for RAG Engineering with revision plans and mock checkpoints.