Generative AI Courses in India Compared (2026)

Short answer: generative AI courses in India fall into three groups:

• Short recorded courses of a few hours that explain LLMs, prompting and RAG.
• Broad AI/ML career programs where GenAI is one part, usually running for several months.
• GenAI engineering programs that focus on building, securing and deploying LLM applications and agents.

If you already work as a developer, data engineer, tester or analyst who codes, the third group is usually the closest fit. If you're starting from zero, a broad program that teaches Python and ML first may suit you better.

What we compared

• Format (live or recorded)
• Duration and live hours, where stated
• GenAI topics named in the syllabus
• Projects
• Who issues the certificate

We didn't rank providers or quote ratings, placement figures, salaries or fees. Fees change often, so check each provider's page.

Side-by-side comparison

Who each option is for

• GUVI's 6-hour GenAI course is a low-cost way to understand LLMs, prompting and RAG before you commit to anything longer.
• GUVI's AI & ML Programme suits beginners and career switchers who want Python, ML and deep learning first, with teaching in several Indian languages. GenAI comes later in the syllabus.
• GUVI's Intel and IITM Pravartak program suits learners who want a long, broad program with institutional co-branding and an in-person immersion.
• The Johns Hopkins program via Great Learning suits professionals who want a university-branded GenAI credential in an online format.
• Datamarcos's GenAI & Agentic AI Engineer program is for people who already write code or work with data and want to engineer production LLM systems, not learn ML from scratch. The projects are a secure Knowledge Copilot, a Multi-Agent Ops Copilot, a production-hardening project and a capstone. If you need to learn Python or ML from the ground up first, a broad AI/ML program is the better first step.

How to tell a GenAI "engineering" course from an "awareness" course

Ask any provider for a sample project brief and check which column it fits. For the security row, the OWASP Top 10 for LLM Applications is a useful reference; for governance, see the NIST AI Risk Management Framework.

Questions to ask before you enrol

1. How many hours are live, and how many are labs?
2. What will I deploy, and where?
3. Is LLM output evaluated (for example with RAGAS), or are there only demos?
4. Who reviews my projects, and how often?
5. What are the prerequisites? Be honest about where you're starting.
6. What exactly does the certificate say, and who issues it?

Where Datamarcos fits

The Generative AI & Agentic AI Engineer program runs for 18 weeks in five phases:

1. Foundations (weeks 1–3): LLMs, prompting, structured outputs and a tested GenAI API
2. RAG systems (weeks 4–7): retrieval, GraphRAG, multimodal documents and evaluation
3. Agentic AI (weeks 8–12): LangGraph, MCP, multi-agent orchestration and human approval
4. Production and security (weeks 13–16): guardrails, LLMOps, fine-tuning and cost, cloud deployment
5. Capstone and career launch (weeks 17–18), ending in a Demo Day defence

It's a career-focused program, not a placement guarantee: hiring outcomes depend on your effort and the market.

Want a shorter start? See the 8-week Applied Agentic AI Certification or our agentic AI courses comparison. Choosing between RAG and fine-tuning for a project? Read RAG vs fine-tuning. More guides are in the guides hub.