RAG Architect 2026

Building Enterprise RAG & Agent Systems

About the Course

Modern AI systems are no longer built by simply calling an LLM API.

In real companies, AI must retrieve knowledge from large document collections, reason across information, stay grounded, remain secure, and scale reliably under cost and latency constraints.

This is why Retrieval-Augmented Generation (RAG) has become the core architecture behind enterprise copilots, intelligent search systems, analytics assistants, and autonomous agents.

RAG Architect 2026 is a 4-week, in-depth live program focused on designing and building production-grade RAG systems the way they are implemented inside modern organizations.

The course emphasizes architecture, engineering decisions, trade-offs, evaluation, and production readiness — not surface-level demos or framework walkthroughs.

Who This Course Is For

This program is designed for:

AI / ML Engineers working with LLMs
Data Engineers moving into GenAI systems
Software Engineers building AI features or copilots
Final-year students preparing for AI roles
Professionals working on internal search, analytics, or knowledge platforms

This course is ideal if you want to understand how RAG systems actually work in production, not just how to use a library.

What You Will Learn

By the end of the program, you will be able to:

Design end-to-end enterprise RAG architectures
Build robust document ingestion pipelines for real-world data
Engineer high-quality retrieval systems (chunking, hybrid search, reranking)
Improve answer quality using query understanding and context engineering
Evaluate and debug hallucinations and retrieval failures
Implement Graph RAG, Multimodal RAG, and Agentic RAG
Apply security, cost optimization, and observability best practices
Design and deploy a production-ready RAG platform

Course Structure

Total Sessions: 11 Live Classes
Duration: 4 Weeks
Schedule: Saturday & Sunday
Time: 8:00 AM IST
Format: Live sessions with hands-on system design and implementation

Curriculum Overview

Class 1: RAG Foundations & Architecture

Understand why RAG is the dominant enterprise AI architecture and how complete RAG systems are structured.

Class 2: Enterprise Document Ingestion

Learn how real-world documents (PDFs, reports, tables, scans) are processed for AI systems.

Class 3: Chunking & Knowledge Structuring

Design intelligent chunking strategies that preserve context and improve retrieval accuracy.

Class 4: Retrieval Engineering

Build high-quality retrieval pipelines using hybrid search, ranking, and relevance tuning.

Class 5: Query Understanding & Context Building

Transform user queries into effective retrieval plans using intent analysis and context construction.

Class 6: RAG Evaluation & Debugging

Measure, analyze, and fix retrieval and generation failures using systematic evaluation methods.

Class 7: Knowledge Graph RAG

Use entity relationships and structured knowledge for multi-hop reasoning.

Class 8: Multimodal RAG

Extend RAG systems to work with images, tables, and video-based knowledge.

Class 9: Agentic RAG Systems

Build RAG-powered agents that can reason, use tools, and maintain memory.

Class 10: Production Engineering

Apply security, cost optimization, monitoring, and scalability best practices.

Class 11: Capstone Project

Design and integrate a complete production-ready RAG system.

Capstone Project

You will build a full enterprise-grade RAG platform that includes:

Real document ingestion
Advanced retrieval and reasoning
One advanced capability (Graph, Multimodal, or Agentic RAG)
Evaluation, monitoring, and security considerations
Deployment-ready architecture

This capstone is portfolio-ready and aligned with real industry expectations.

What You’ll Walk Away With

Strong understanding of enterprise RAG system design
Practical experience with production AI engineering
Confidence to discuss RAG architecture in interviews
A realistic capstone project for your portfolio