MODULE 013 HOURS
GenAI & LLM Foundations
- LLMs, tokens, context windows and model selection
- Prompting, instructions and structured output
- Reliability, privacy and responsible AI
Guided lab: Build and compare structured prompt responses
GENAI ENGINEERING · LIVE ONLINE 1:1 · TAMIL-FRIENDLY
Generative AI Engineering with RAG & AI Agents
From LLM APIs to real AI applications.
30 hours of personalized, project-led training in Python, RAG, vector databases, LangChain, LangGraph, MCP and safe agent workflows. Build an enterprise knowledge assistant with practical instructor feedback.
30 Instructor-Led Hours9 ModulesRAG + Agents + MCPCapstone Project
Python basics recommended. API/hosting charges may be separate; schedule and training fees confirmed on enquiry.
GENAI ENGINEERING STUDIOINTERACTIVE DEMO
ENTERPRISE KNOWLEDGE ASSISTANT
From documents to trusted action.
Explore how RAG and AI agents work together.
PDFsEmbeddingsLLMTools
Documents → Chunks
Prepare PDFs and text with chunking, metadata and embeddings.
Human-verified outcomes. Evaluate answers, inspect sources and approve sensitive actions.
Concept simulation; no data or tools are executed
Know before you enquire:Live online • Dedicated trainerPython basics required30 instructor-led hoursFee and schedule on enquiryAPI usage may cost extra
30 Hours
Structured instructor-led lessons
Live 1:1
Personal guidance and reviews
Build RAG
Retrieval, citations and evaluation
Agentic AI
Tools, MCP and safe orchestration
COURSE AT A GLANCE
Learn to engineer GenAI systems, not just write prompts.
MaanavaN's Generative AI Engineering with RAG & AI Agents is a 30-hour live one-to-one online program for Python-ready learners, developers and IT professionals. It teaches LLM APIs, embeddings, vector search, document RAG, evaluation, tool-using agents, LangGraph, MCP and FastAPI through one connected project. Tamil-friendly guidance is available online for Chennai, Coimbatore, other parts of Tamil Nadu and Tamil-speaking learners worldwide.
TECHNOLOGY STACK
Tools and frameworks you will practise
One coherent stack, meaningful practical depth. Alternative providers are introduced as comparisons—not separate full courses.
Python
Application foundation
LLM APIs
Model integration
LangChain
Retrieval pipelines
LangGraph
Agent orchestration
Chroma
Vector search
MCP
Tool connectivity
FastAPI
Backend APIs
Streamlit / UI
Practical interface
Evaluations
Quality and safety
Git & GitHub
Source control
STRUCTURED 30-HOUR CURRICULUM
Nine connected modules. One working GenAI application.
Move from LLM APIs to vector retrieval, RAG, agent tools, evaluation and delivery. Every stage contributes to the final capstone.
MODULE 023 HOURS
Python & LLM API Development
- Python HTTP/API integration and SDKs
- Environment variables, errors, streaming and response handling
- Token usage and cost-conscious development
Guided lab: Build a Python LLM API client with validation
MODULE 033 HOURS
Embeddings & Vector Databases
- Text chunking and metadata
- Embeddings and semantic similarity
- Chroma vector store; FAISS concepts
Guided lab: Index sample documents and run semantic search
MODULE 044 HOURS
RAG Application Development
- Document loading and indexing pipeline
- Retrieve context and generate grounded answers
- Source attribution and missing-evidence handling
Guided lab: Create a source-cited document Q&A assistant
MODULE 053 HOURS
Advanced RAG & Evaluation
- Hybrid retrieval and metadata filtering
- Reranking and query transformation concepts
- Small evaluation datasets and retrieval quality checks
Guided lab: Compare baseline and improved retrieval results
MODULE 063 HOURS
AI Agents & Safe Tool Calling
- Tool/function calling and agent patterns
- Validation, retries, permissions and approval steps
- Agent failures and prompt-injection boundaries
Guided lab: Build an agent with one controlled ticket-draft tool
MODULE 074 HOURS
LangChain, LangGraph & MCP
- LangChain components and runnable pipelines
- LangGraph state, branching and execution flow
- MCP tool integration fundamentals
Guided lab: Orchestrate a RAG + tool workflow with explicit approval
MODULE 083 HOURS
FastAPI & GenAI App Delivery
- Expose an AI application through FastAPI
- Connect a provided UI or simple Streamlit interface
- Logging, secrets, deployment and cost controls
Guided lab: Run and test a documented AI application API
MODULE 094 HOURS
Final Capstone & Review
- Build an enterprise knowledge assistant with citations
- Connect one authorized tool action
- Test failures, safety, quality and deployment readiness
Guided lab: Present a working demo, code, architecture and evaluation report
BUILD RESPONSIBLY
Reliable answers. Safe tools. Verified outcomes.
Learn to test grounded answers, inspect retrieval failures, prevent unauthorized tool execution and review prompt-injection risks. The program emphasizes observability and human approval rather than blindly trusting model output.
1Check answers against cited documents
2Measure retrieval precision with test questions
3Keep API keys and data private
4Require approval before agent actions
CAPSTONE PROJECT
Build an Enterprise AI Knowledge Assistant
Index sample documents, answer questions with source citations, add one approved ticket-draft action and test the system with a small evaluation set. Expose it through FastAPI and a simple interface. Finish with source code, an architecture diagram, test evidence and deployment notes.
Document ingestion + RAG
Evidence-based answers
Approved agent tool
Evaluation and API demo
Who should join this program?
Python developers building real AI applications
Software engineers moving into GenAI engineering
Data and IT professionals with Python fundamentals
PERSONALISED 1:1 ENQUIRY
Find the right GenAI learning plan for you.
Tell us a little about your background. Your WhatsApp message will include your interests so we can discuss training fit, scheduling and fees.
30 hours · Python fundamentals recommended · Online Tamil-friendly instruction
This opens WhatsApp with a prepared message. Nothing is sent until you choose to send it. No placement or job outcome is guaranteed.
Discuss My Training Plan Opens WhatsApp; training fee and schedule are confirmed through enquiry.
Frequently asked questions
Who is this GenAI training for?
Developers, data professionals and IT employees who want to build practical GenAI applications. Familiarity with Python functions, packages and APIs is recommended.
Is this a beginner-friendly program?
Beginners to GenAI can join if they know basic Python. Complete programming beginners should first take a Python foundation course.
What is the duration and format?
The core program has 30 hours of live one-to-one instructor-led training across nine modules. Scheduling is agreed individually.
What projects will I build?
You progressively build an enterprise knowledge assistant using document retrieval, cited answers, a controlled tool and an API interface.
Does the program include RAG and AI Agents?
Yes. Basic and advanced retrieval, vector databases, agent tool calling, LangChain, LangGraph, MCP fundamentals and evaluation are core modules.
Will I learn to deploy an application?
You will build a FastAPI-based application and cover deployment basics, logging, secrets and cost controls. Production operations depth is an optional advanced extension.
Is the training available in Tamil?
Live sessions use Tamil-friendly explanations alongside the English terminology used in software engineering. Learners can attend online from India or overseas.
Will there be extra costs for LLM APIs?
Model API usage, hosting or other third-party tools may incur charges. The instructor guides budget limits and safe handling of credentials; fees are confirmed on enquiry.
Does this guarantee placement or a certification?
No. This is a practical engineering program. It is not a job guarantee or an official vendor certification.
START YOUR GENAI ENGINEERING JOURNEY
Build production-minded AI skills with 1:1 guidance.
Tell us your Python experience, project goal and preferred training schedule.