Intermediate · Live Online · 3 days

AI Product Engineering Bootcamp

Build and deploy a real AI application in 3 days — LLMs, RAG, agents, APIs and production architecture. Not prompts.

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Live, hands-onYour own GitHub repoVerifiable certificate

Duration3 days · 4 hrs/day · 12 hours live

LevelIntermediate

FormatLive Online

Next batchDates announced soon

Fee₹2,999

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Why this bootcamp

Calling an LLM API is easy. Building a reliable AI-powered product is not.

This bootcamp is for engineers who already use ChatGPT, Copilot and LLM APIs, and want to understand the engineering underneath: how a user problem becomes an AI use case, how that becomes an architecture, and how that architecture survives contact with real data, real cost and real users.

Over three days you design, build and deploy a working AI application. You leave with the code, the architecture and a deployed URL — not a folder of slides.

What you'll build

                  USER
                    |
                    v
             Web Application
                    |
                    v
                API Layer
                    |
          +---------+---------+
          v                   v
     Application           AI Layer
        Logic                 |
                              v
                       AI Orchestration
                              |
              +---------------+---------------+
              v               v               v
             LLM             RAG             Tools
              |               |               |
              v               v               v
          AI Model        Vector DB      External APIs

Syllabus

Day by day

Day 1

From LLM to a real application

How modern AI applications actually work: tokens, context, embeddings, retrieval, agents
Why ChatGPT is not an AI product
Python + FastAPI + LLM APIs (OpenAI / Azure OpenAI / Gemini / Claude)
Structured output and function/tool calling
Build: an AI Document Intelligence Assistant — upload a PDF, ask questions, get answers with citations
Engineering it properly: API design, error handling, prompt versioning, cost, latency, logging
Outcome: a working AI application in your own GitHub repository

Day 2

RAG and agents over your own data

Embeddings, chunking strategies and what actually breaks retrieval
Vector databases, metadata filtering and hybrid search
RAG architecture end to end — and how to evaluate it
Hallucination: where it comes from and what reduces it
Agent architecture: tools, planning, multi-step workflows
Build: an AI Engineering Assistant that answers questions about a real codebase
"Where is authentication implemented?" · "What happens when a customer creates an order?" · "Explain this module."

Day 3

Production and deployment

Agent orchestration and MCP concepts
Human-in-the-loop and when to require it
Evaluation, guardrails and failure modes
Observability, security and cost optimisation
Docker and cloud deployment
Build & deploy: an AI agent that plans, searches code, calls APIs, generates code and runs tests
AI Product Challenge + demo day — 5-minute demos, awards for best product, engineering and agent

Topics covered

LLM APIsStructured outputFunction / tool callingFastAPIEmbeddingsChunkingVector databasesRAG architectureHybrid searchRAG evaluationAI agentsAgent orchestrationMCPGuardrailsObservabilityCost & latencyDockerCloud deployment

Outcomes

What you'll be able to do

Identify where AI genuinely adds value in a product — and where it does not
Turn a business problem into an AI use case and an architecture
Build AI-powered APIs and integrate them into backend services
Design and evaluate a RAG system over your own data
Decide correctly between RAG, a workflow, tool calling and a full agent
Handle hallucination, reliability, cost and latency as engineering problems
Deploy an AI application and reason about observability and security

Before

After

I use ChatGPT and Copilot, but I do not know how an AI product is actually built

→

I understand the components of an AI product and how to design an AI-powered application

I know RAG and agents as buzzwords

→

I understand when to use RAG, workflows, tools or agents

I can call an LLM API

→

I understand how to integrate AI into a software product

I have no AI work to show

→

I have a deployed AI application and a public repository

Who it's for

Built for people who build software

Software & Backend DevelopersMoving into AI application development

Full-Stack DevelopersBuilding complete AI-powered products

Python & Java DevelopersBuilding LLM systems and enterprise AI integrations

ML & Data EngineersExpanding into LLM, retrieval and product engineering

Tech Leads & ArchitectsUnderstanding how AI systems fit into product architecture

Final-year & postgrad studentsWelcome if you're comfortable coding — this is the deeper track

This is not…

A ChatGPT tutorialA prompt engineering courseA theoretical AI courseA mathematics-heavy ML courseA collection of AI slidesA certificate-only programme

Your instructor

Dr Ganesh

AI Product Engineer · Generative AI, AI Systems & Software Architecture

Dr Ganesh works on practical engineering problems involving generative AI, LLM applications, AI agents, RAG systems, knowledge graphs, software architecture, code intelligence and application modernisation.

His approach is simple: don't just learn AI tools — learn how AI systems are engineered. This workshop covers practical engineering concepts and general industry knowledge, and does not expose proprietary client or employer intellectual property.

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FAQ

Questions people ask

Is this suitable for beginners?

Yes, if you already have basic programming or software development experience. This is not a first programming course.

I'm a student — can I join this instead of the Developer Bootcamp?

Yes. The tracks are separated by depth, not by job title. If you code confidently and want architecture, RAG evaluation and deployment rather than fundamentals, this is the right one.

Do I need Python?

Python familiarity helps, but the workshop focuses on AI engineering concepts and architecture that apply across technology stacks.

Is this only about ChatGPT?

No. We work across LLM providers and focus on the engineering patterns rather than one vendor's API.

Will I get a certificate?

Yes — a Certificate of Completion with a verifiable link you can share on LinkedIn.

Will I learn everything about AI in 3 days?

No, and we will not claim that. The goal is a strong practical foundation in AI product engineering, and a clear picture of what to build next.

Can I ask questions during the sessions?

Yes. Batch sizes are limited specifically to keep the sessions interactive.

What do I need installed?

A laptop, a browser and VS Code. We send a setup guide and API key instructions before Day 1.

Are sessions recorded?

Yes, recordings are shared with participants after each day.

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You should walk out able to build the thing, not just talk about it.

3 days · 4 hrs/day · 12 hours live · Live Online · ₹2,999

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