RAG Engineering: From Prototype to Production

Build Production-Grade RAG Systems for Reliable, Grounded AI

Move beyond the basic “documents → embeddings → vector database → LLM” approach and learn how to engineer RAG systems designed for relevance, accuracy, grounding, traceability and reliability.

This hands-on 2-week specialization takes you through the complete RAG engineering lifecycle—from transforming real-world data into AI-ready knowledge to advanced retrieval, context engineering, production architecture and RAG evaluation.

You will also explore the major RAG architectures and patterns used to solve different real-world problems, including Naive RAG, Advanced RAG, Hybrid RAG, Corrective RAG, Self-RAG, Adaptive RAG, Graph RAG and Agentic RAG.

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What You’ll Learn

🧠 LLM & Context Engineering

Understand the role of the LLM within a RAG system:

• Model selection
• Context windows
• Token economics
• Structured outputs
• Context optimization
• Query understanding and rewriting
• Grounding techniques

📚 Enterprise Data → AI Knowledge

Learn how raw data becomes useful knowledge for an AI system:

• Document ingestion
• Document processing
• Content cleaning
• Chunking strategies
• Metadata design
• Embeddings
• Knowledge pipelines

🔎 Advanced Retrieval Engineering

Go beyond basic similarity search:

• Semantic search
• Metadata filtering
• Hybrid search
• Multi-query retrieval
• Query transformation
• Reranking
• Retrieval optimization

🏗️ RAG Architectures & Patterns

Understand when and why different RAG approaches are appropriate:

• Naive / Basic RAG
• Advanced RAG
• Modular RAG
• Hybrid RAG
• Corrective RAG (CRAG)
• Self-RAG
• Adaptive RAG
• Graph RAG
• Agentic RAG
• Multi-Query RAG
• Hierarchical RAG
• Conversational RAG
• Multimodal RAG

🏢 Production RAG Architecture

Learn how to design RAG systems beyond a simple prototype:

• Vector databases
• Retrieval pipelines
• Context assembly
• Citations
• Source attribution
• Access-aware retrieval
• Scalable architecture
• Failure handling

🛡️ RAG Quality & Reliability

Learn how to determine whether your RAG system is actually producing trustworthy results:

• Retrieval relevance
• Context relevance
• Answer relevance
• Faithfulness
• Groundedness
• Hallucination controls
• RAG evaluation
• Failure analysis
• Observability

🤖 AI-Assisted RAG Development

Use modern AI coding agents such as Codex, Claude Code, GitHub Copilot and Cursor to accelerate development, debugging, testing and optimization of your RAG application.

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💻 What You’ll Build

You will build a production-oriented RAG application following the complete pipeline:

Enterprise Data → Processing → Chunking → Embeddings → Retrieval → Reranking → Context Engineering → LLM → Grounded Response → Citations → Evaluation

The goal is not simply to build a chatbot that can search documents.

The goal is to understand how to engineer a RAG system and improve it when retrieval, grounding or response quality is not good enough.

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🎯 Who This Course Is For

Software Developers, AI/ML Engineers, Data Engineers, Architects, Tech Leads and developers who want to move beyond basic RAG implementations and learn how to design reliable, production-oriented RAG systems.

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🌟 What You’ll Walk Away With

By the end of the specialization, you will have:

• A clear understanding of modern RAG architecture
• Knowledge of major RAG patterns and architectures
• Practical experience with advanced retrieval
• Understanding of chunking and embedding strategies
• Experience with semantic and hybrid search
• Understanding of reranking and query transformation
• Knowledge of Graph RAG and Agentic RAG
• Experience implementing citations and source attribution
• Understanding of RAG evaluation and reliability
• Experience troubleshooting retrieval and grounding problems
• A production-oriented RAG application
• Practical experience using AI coding agents for RAG development

The Outcome

Move beyond “Vector DB + LLM” and learn how to engineer RAG systems that are relevant, grounded, traceable, evaluable and production-ready.

📅 Program Format

2 Weeks | 4 Instructor-Led Live Zoom Sessions | Tuesdays & Thursdays | 8:00–9:30 PM IST

6 Hours Live Training + Hands-On Project