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A builder's primer on how retrieval grounds language models — the pipeline, the components, the design decisions, and a complete curated study library.
Saturday, 11 July 2026 · 5:00 PM – 8:00 PM IST · 3 hours live · recording included
Problem
Stale knowledge
RAG fix
Update the index, not the model weights
Problem
No provenance
RAG fix
Retrieve evidence, cite sources
Problem
Hallucination
RAG fix
Generate only from retrieved context
Every production RAG system — however sophisticated — is built from these stages.
Ingestion
PDFs, HTML, DBs → clean text + metadata
Chunking
256–512 tokens, semantic or recursive split
Embedding
Dense vectors via domain-matched model
Indexing
Vector DB with HNSW / hybrid index
Retrieval
Top-k chunks via dense + sparse hybrid
Reranking
Cross-encoder precision pass
Generation
Grounded answer + citations
“Garbage retrieved in, garbage generated out.” — Most production effort goes into stages 2–6, not the prompt.
Naive RAG
Embed → retrieve → generate. Fast baseline.
Advanced RAG
Query rewrite + reranking. Fixes precision.
Modular RAG
Swappable routing, hybrid search, memory.
Agentic RAG
LLM decides when and what to retrieve.
GraphRAG
Knowledge graph traversal for corpus-wide Q&A.
Start with the free primer. Join the live masterclass when you're ready to build.