Generative AI Engineering & RAG Systems
Build production-grade Generative AI applications, Retrieval-Augmented Generation (RAG) pipelines, and autonomous AI agents using LangChain, LlamaIndex, and Vector Databases.
Course Overview
Master enterprise Generative AI engineering. Learn to integrate LLM models (Azure OpenAI, Hugging Face, Llama 3), build advanced RAG search systems, index data in vector databases (Pinecone, Qdrant, ChromaDB), and construct autonomous AI agent workflows.
Quick Facts
Who Should Join
Target Audience
- Software developers embedding AI features into products
- Data engineers building enterprise GenAI pipelines
- AI enthusiasts seeking practical hands-on LLM engineering skills
Expected Baseline Knowledge
- Intermediate Python programming knowledge
- Familiarity with REST APIs
- Basic understanding of JSON and data handling
What You Will Learn
Curriculum Modules
3 ModulesEach module combines theoretical concepts, practical coding exercises, and milestone assessments.
Hands-on Student Projects
Clearly structured practice exercises and end-to-end simulated capstone projects.
Practice Project: PDF Document Q&A Bot
Problem Addressed: Extract quick answers from dense PDF policy manuals.
A Python RAG application searching embedded PDFs and returning cited answers.
- RAG Python code
- Sample test suite
Simulated Business Project: Autonomous Enterprise Data & Research Agent
Problem Addressed: An enterprise requires an autonomous AI agent that accepts research questions, queries internal SQL databases, conducts vector searches on technical docs, and drafts executive briefing reports.
A multi-tool AI agent built with LangChain, Azure OpenAI, PostgreSQL, and FastAPI with web interface.
- Full application code repository
- Agent evaluation benchmark log
Class Experience & Learning Environment
Live Sessions
Live interactive AI engineering labs with instructor guidance.
Recordings & Access
All session recordings uploaded with lifetime access.
Assignments & Work
Weekly GenAI prompt engineering and Python challenges.
Doubt & Support
Private Slack channel for technical assistance.
Lead Industry Trainer
Dr. Rajesh Kumar
AI Architect & Lead GenAI Researcher
10+ Years in AI/ML & Cloud Systems Architecture
Schedule & Transparent Fees
Starts 2nd Monday of Next Month
Weekend Batch: Sat & Sun 2:00 PM - 5:00 PM
Standard fee with 2-installment plan option
Full refund prior to 2nd live class.
360° Career Assistance Scope
Substantiated referral assistance and technical preparation without misleading promises.
GenAI developer resume review emphasizing RAG and LLM agent projects.
2 GenAI technical mock interviews covering RAG architecture and prompt engineering.
GenAI interview question bank and coding challenges.
Referral opportunities to hiring partners.
Support Duration: 6 Months career support post-course.
- Complete all GenAI labs
- Build and demo Capstone project
- Pass mock interview