The AI Engineering Program: Module 8 of 13
RAG Engineering
Build retrieval systems that answer from company documents with citations, in English and Indian languages, and prove how often they retrieve the right source.
30guided hours
13lessons
Onlineself-paced
Lessons
Every lesson: deep notes, curated lectures, an animated explainer, a hands-on lab, practice questions and a timed exam.
- Why RAG, and when not to use it
- Parsing real documents: PDFs, tables and scanned pages
- Chunking strategies and their trade-offs
- Embeddings and vector databases (Chroma, Qdrant, pgvector)
- Hybrid search: keywords plus vectors
- Reranking
- Query rewriting and routing
- Grounded answers with citations
- Multilingual RAG for Indian languages
- Text-to-SQL: answering questions from databases
- Graph-based retrieval: an introduction
- Measuring retrieval: recall@k and MRR
- Module project
Tools introduced: Vector databases, IndicTrans2
Module project
Bilingual policy assistant: answers questions about public Indian government documents in English and Hindi with citations, plus a retrieval-quality report.