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A document-aware AI assistant powered by a RAG pipeline.
Understand where LLMs and RAG fit into modern AI applications.
No bloated curriculum. Every part connects architecture, implementation, and the next project for your AI transition.
Join NowAI Engineer Roadmap
Where RAG fits into modern AI Engineering.
LLMs + RAG
Understand why LLMs alone aren't enough for many real applications.
Build the RAG Architecture
Documents → chunks → embeddings → vector database → retrieval → response.
Python Implementation
See the architecture translated into working application code.
Software professionals who attended the live workshop.
The architecture walkthrough finally made RAG click for me. I left with a clear picture of how the whole pipeline fits together.
Aakash Mishra
Software Engineer · 10 yrs
Worth it for the roadmap alone. It gave me a concrete next-90-days plan to transition into AI engineering.
Pooja Dubey
Backend Developer · 6 yrs
Practical, dense, and no fluff. Seeing the Python implementation live made the architecture real instead of abstract.
Anshul Sharma
QA Automation Engineer · 5 yrs
Stop watching another AI tutorial. Spend 2 practical hours understanding how a real RAG system works.
Opens in WhatsApp · Instant access to the group
Understand the complete pipeline from documents to grounded answers.
Understand chunking, embeddings, vector databases, and retrieval.
See how the architecture translates into actual application code.
Understand the difference between a demo and a production-style system.
Understand what to learn next as you transition toward AI Engineering.
Your Next 90 Days
Understand what to learn and build next.
Live Q&A
Bring your AI Engineering questions.
Built from real-world experience
Abhishek Jain, Lead Engineer at NatWest, brings 13+ years of experience building scalable enterprise systems and mentoring engineers through real-world architecture and system design.
This workshop goes beyond demos and frameworks — you’ll learn the engineering principles behind building reliable, production-ready RAG systems that can serve as the foundation for AI Agents and Agentic AI.
“Don't just learn how to use AI tools. Learn how to engineer the systems behind them.”