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