RAG Training India — Retrieval-Augmented Generation for Developer Teams
Most developer teams can follow a RAG tutorial. Very few can ship a RAG system that stays accurate in production. This training is the bridge — from understanding the architecture to deploying a system your company actually relies on.
What is RAG?
Retrieval-Augmented Generation (RAG) is an AI architecture that connects a large language model (LLM) to your organisation's own data — documents, databases, wikis, product manuals — so the AI can answer questions using your specific information rather than its generic training data.
Without RAG, an LLM only knows what was in its training data. With RAG, it can retrieve the exact paragraph from your internal policy document, the latest pricing from your database, or the right clause from a contract — and answer questions about it accurately.
RAG is the most widely deployed LLM pattern in enterprise production systems. Every company building an internal knowledge assistant, document Q&A system, HR policy bot, or customer support AI is building RAG. The question is whether they build it well.
of enterprise RAG deployments fail retrieval benchmarks in production
faster document Q&A with properly tuned RAG vs keyword search
model retraining cost — RAG updates in real time as your data changes
What RAG Training Covers
A complete RAG training programme goes far beyond the quickstart tutorial. These are the topics that separate a working demo from a production system.
RAG Architecture
Understand the full retrieval pipeline — chunking, embedding, indexing, retrieval, reranking, and generation. Know why each step exists.
Vector Databases
Build with Pinecone, ChromaDB, and FAISS. Understand when to use each and how to tune retrieval quality for your data.
LangChain & LlamaIndex
Implement RAG pipelines with both major frameworks. Understand their trade-offs so your team can choose confidently.
Advanced Retrieval
Multi-query retrieval, hybrid search (sparse + dense), metadata filtering, and contextual compression — the patterns that fix brittle pipelines.
RAG Evaluation
Measure retrieval quality and generation faithfulness with RAGAS. Build an evaluation harness before you deploy.
Production Deployment
Deploy your RAG system as a FastAPI service, handle latency, manage costs, and monitor in production.
Why your team's RAG pipeline breaks in production
The LangChain quickstart gets you a working RAG chatbot in 20 minutes. Then you try it on your actual company data — PDFs with tables, documents with mixed languages, knowledge bases with 50,000 records — and it falls apart. Retrieval quality drops. The LLM hallucinates. Latency spikes.
The gap between tutorial RAG and production RAG comes down to four things: chunking strategy (most tutorials use naive fixed-size chunking that destroys context), retrieval quality (cosine similarity alone is not enough for real enterprise queries), evaluation (you cannot fix what you cannot measure), and operational monitoring (RAG quality degrades silently as your data changes).
The RAG training module inside our Production AI Engineering programme covers all four in depth — with hands-on labs on real datasets, not the sample PDFs from the documentation.
RAG Training — Frequently Asked Questions
What is RAG (Retrieval-Augmented Generation)?+
RAG (Retrieval-Augmented Generation) is an AI architecture that connects a large language model to your own data — documents, databases, knowledge bases — so it answers questions using your specific information rather than generic training data. It eliminates hallucination on domain-specific queries, keeps responses current without retraining the model, and is the most widely deployed LLM pattern in enterprise production systems.
How long does RAG training take for a developer team?+
A structured corporate RAG training programme runs 2 days (16 hours) for a focused deep-dive, or is delivered as part of the full 5-day Production AI Engineering course. Two days is enough for developers to build and deploy a working RAG pipeline. The full 5-day programme adds agents, tool calling, and MCP on top of RAG foundations.
What tools are covered in RAG training?+
Production RAG training covers LangChain and LlamaIndex as orchestration frameworks, Pinecone, Weaviate, ChromaDB, and FAISS as vector databases, OpenAI and open-source embedding models, and RAGAS for RAG evaluation. Participants also learn chunking strategies, metadata filtering, hybrid search, and reranking — the techniques that separate working RAG from production RAG.
Is RAG training available online or only on-site in India?+
Both. RAG training is available as an on-site programme at your office across India (Bangalore, Mumbai, Hyderabad, Delhi, Pune, Chennai, and more) and as a virtual instructor-led programme for distributed teams. Both formats use the same hands-on, project-based structure.
Ready to run RAG training for your team?
Tell us your team size, Python level, and what you want to build. We'll send a customised proposal within 24 hours.
RAG training is available standalone or as part of the full Production AI Engineering programme.