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GenAI & Agentic AI Engineering Toolkit

A hands-on, two-day introduction to the modern AI engineering stack. You will call models from Python, get reliable structured output, connect AI to real functions, and build your first tool-using agent, all around one realistic customer-support use case.

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12 hours live5 hands-on labsIndustry use caseLive & mentor-guided
Duration2 days · 12 hours live
FormatLive online · hands-on
Fees & datesShared on WhatsApp
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The industry use case

One real problem, start to finish

Every concept in this workshop is applied to the same realistic scenario, so you learn how the pieces fit together, not just what they are.

The company

An e-commerce company receiving 3,000 support tickets a day

The problem

Agents spend hours reading, tagging and replying to repetitive tickets about orders, refunds and delivery.

What you build

A ticket-triage API that classifies and extracts details, and a support agent that looks up the order, checks the refund policy and drafts a resolution, escalating to a human when unsure.

Who it's for

Is this workshop for you?

Final-year & postgrad studentsMove from "I use ChatGPT" to "I build with LLMs", with a project to show.
Software developersAdd GenAI and agents to the skills you already have.
QA & test engineersUnderstand how AI apps work so you can build and test them.
Frontend developersLearn the backend AI layer your features will call.
Faculty & trainersGet a structured, practical view of the ecosystem to teach from.
IT professionals moving into AIA clear map of the field before you invest months in it.

Before you join

  • No programming experience required: we cover the Python you need, step by step
  • A laptop and a stable internet connection
  • No prior AI or machine-learning background needed

Open entry: anyone can join.

This is not

  • A ChatGPT tips session
  • A theory or maths lecture
  • A list of tools without practice
Curriculum

2 days · 10 sessions · 5 hands-on labs

Day 1

Know the stack, build with LLMs

  1. How GenAI applications actually work
    • Tokens, context windows, temperature and cost
    • Model families (OpenAI, Claude, Gemini, open models) and how to choose
    • Where AI adds value in a product, and where it does not
  2. Python + LLM APIsLab
    • Your first model call from Python
    • System prompts, prompt templates and treating prompts as code
    • Handling errors, timeouts and rate limits
  3. Structured output you can trustLab
    • JSON schemas and Pydantic models
    • Extracting order ID, category and urgency from raw tickets
    • Validating and retrying when the model gets it wrong
  4. Tool / function callingLab
    • Letting the model call your Python functions
    • Connecting to an order-status API
    • Secrets, environment config and webhooks
  5. AI-assisted development, responsibly
    • IDE assistants, coding agents and terminal agents
    • Reviewing and testing AI-written code instead of trusting it
By the end of Day 1: Ticket-triage API (FastAPI): category, priority, extracted fields and a draft reply for every ticket.
Day 2

Think in agents

  1. Anatomy of an agent
    • Model + instructions + tools + state + memory + control loop
    • Why most "agents" should really be workflows
  2. Workflow, agent or multi-agent?
    • A decision framework you can reuse
    • Thinking exercise: classify six real industry scenarios and defend your choice
  3. The framework landscape
    • LangChain, LangGraph, CrewAI and Google ADK: what each is for
    • We implement with LangGraph and show the others for comparison
  4. MCP fundamentalsLab
    • Connecting AI apps to tools and data through MCP servers
    • Exposing your order and policy lookups as MCP tools
  5. Build: support resolution agentLab
    • Looks up the order, checks the refund policy, decides, drafts the reply
    • Escalates to a human when confidence is low
By the end of Day 2: A tool-using support agent, plus your personal AI tool-selection playbook.
Tools & stack
PythonOpenAI / Claude / Gemini APIsPydanticFastAPILangGraphMCPVS Code + AI assistant
Inside the labs

See what a lab looks like

Real recordings from our hands-on labs: code, running apps and measured results.

RAG lab

A documentation assistant, built end to end

A lab project: FastAPI backend, Chroma vector store and Llama 3 via Groq, answering questions about LangChain documentation.

RAGFastAPIChromaLlama 3
RAG evaluation lab

Measuring answer quality, not guessing

A lab notebook: a local FAISS knowledge base, a RAG pipeline on Groq LLMs, and RAGAS metrics to evaluate the answers. Shown at 2.5× speed.

RAG evaluationRAGASFAISSGroq
Outcomes

After two days, you will be able to…

  • Explain how an LLM application works, end to end, in plain terms
  • Call any major model from Python and get validated, structured output
  • Connect an LLM to your own functions and APIs with tool calling
  • Decide between a simple workflow, a single agent and a multi-agent design
  • Build a working tool-using agent with LangGraph and MCP
You leave with
GitHub repository with 2 working projects
AI tool-selection playbook (PDF)
Course materials & lab notebooks
Verifiable certificate of completion
Next stepStep 2 · Build AI Applications: RAG & Agentic WorkflowsBuild your first complete AI application →
Your mentor

Taught live by a practising AI engineer

Dr. Gajanan

Dr. Gajanan

Lead Mentor · Machine Learning, Generative AI & Agentic AI

14+years in ML & AI
ML→GenAI→Agentsthe full arc of modern AI

Designs the curriculum and leads the hands-on labs, helping participants think through problems the way an AI engineer does instead of memorising tools.

Machine learningGenerative AIAgentic AI systemsPython for AI
FAQ

Questions about this workshop

Do I need programming experience?

No. We cover the Python you need, step by step, during the workshop.

Do I need a paid AI subscription?

No. We use free tiers and provide guidance on low-cost API keys; most participants spend nothing or a few rupees on API usage.

I am a tester / frontend developer. Is this for me?

Yes. The toolkit is designed for anyone who writes some code. You will understand the AI layer well enough to build and test it.

What materials do I get?

Course notes, lab notebooks, project templates and the AI tool-selection playbook, yours to keep.

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Explore: GenAI & Agentic AI Engineering Toolkit

Share your details. Cohort dates and a secure Razorpay payment link arrive on WhatsApp. Nothing is charged until you confirm.

Fees & datesOn WhatsApp, within minutes
Duration2 days · 12 hours
  • Full refund if you cancel 48+ hours before the start
  • Course materials and a verifiable, LinkedIn-ready certificate
  • Planning more than one workshop? Talk to us

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Your first working AI agent is two days away.

2 days · live online · hands-on

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