AI Developer (Level 7)
Topic Guide · 2026
RAG (Retrieval-Augmented Generation)
RAG guide — build a chatbot on your own docs using embeddings, vector search, reranking and grounded LLM generation.
Quick Answer
RAG (Retrieval-Augmented Generation) grounds an LLM's answers in your own documents. It is the #1 pattern for building safe, accurate enterprise AI in 2026.
What you'll learn
- Ingestion: chunking, embeddings, metadata
- Vector databases (Pinecone, Qdrant, Weaviate, pgvector)
- Retrieval: BM25, dense, hybrid, reranking
- Prompt engineering for grounded answers
- Evals: faithfulness, relevance, groundedness
- Advanced: agentic RAG, GraphRAG
Recommended tools
PineconeQdrantWeaviatepgvectorCohere RerankLlamaIndex
Frequently asked questions
What is RAG in AI?+
Retrieval-Augmented Generation — a pattern where the LLM first retrieves relevant chunks from your knowledge base, then answers using only that context. It removes hallucination and adds citations.
How much does building a RAG chatbot cost?+
For a 10,000-document company knowledge base: ₹1.5–4 lakh setup, then ₹15–50k/month running cost. Cheaper if self-hosted on Qdrant + open embeddings.
Want RAG (Retrieval-Augmented Generation) training for your team?
Nirmal Rabari delivers this topic as a live corporate workshop across India, UAE, UK and US.