How to Choose a Generative AI Course in India (2026): A 10-Point Checklist

Search for "generative AI course" or "agentic AI course" in India and you will find options priced anywhere from a few hundred rupees to well over a lakh. Many of them have nearly identical syllabus pages, and almost all of them promise to make you "job-ready".

Google's research with Kantar found that a majority of Indian learners buying online courses feel overwhelmed by the number and similarity of options (Think with Google, 2026). If that is you, this checklist will help you compare courses on what actually matters.

(Yes, we run a generative AI course ourselves. Use this checklist on ours too.)

Step 0: be clear about what you want

Before comparing courses, answer one question: what do you want to be able to do at the end?

• "Use AI tools at work": you need an AI fluency or productivity course, often a short workshop. You do not need a 12-week engineering course.
• "Automate business tasks without coding": look at AI automation courses using tools like n8n or Power Automate.
• "Build AI applications and agents as a developer": you need an engineering course with real code, which is what the rest of this checklist focuses on.
• "Switch careers into AI from scratch": you need a longer program that also covers programming and software fundamentals.

Buying the wrong type of course is the most common and most expensive mistake.

The 10-point checklist

1. Does the syllabus go beyond prompting?

Prompt engineering alone is now a commodity skill. A serious generative AI course for developers should cover:

• LLM APIs and structured output
• Embeddings, vector databases and RAG
• Tool calling, agents and a framework such as LangGraph
• MCP (Model Context Protocol)
• Deployment

If half the syllabus is "50 prompts for productivity", it is not an engineering course.

2. Does it teach evaluation?

This is the clearest sign of a course built by people who have shipped AI systems. Ask: "How will I learn to measure whether my RAG system or agent is actually correct?" If the answer is vague, move on. Evaluation is the skill that separates demos from production.

3. Does it cover security?

Look for prompt injection, the OWASP Top 10 for LLM Applications, data privacy (including India's DPDP Act) and safe tool design. Agents that take actions without these are a liability, and interviewers know it.

4. Will you build real projects, or follow along?

"10+ projects" often means ten copy-along notebooks. Ask:

• Will I build something of my own design?
• Will my code be reviewed by a mentor?
• Will I deploy it, not just run it in a notebook?

5. Who are the instructors?

Ask what the instructors have built and shipped with LLMs. Have they deployed RAG or agent systems that real users depend on? Course quality in a field this new depends heavily on practical experience.

6. Live classes or recorded videos?

Recorded courses are cheaper and flexible, but completion rates are low and you cannot ask questions. Live classes cost more and give you accountability and help when you get stuck, which you will. If you are working, look for weekend live classes plus weekday labs.

7. How current is the content?

The field moves quickly. Check that the course mentions current topics (agents, MCP, evaluation, AgentOps) and ask when it was last updated. Tools change; concepts last. A good course teaches the concepts using current tools.

8. Are the prerequisites honest?

A course that promises to take you from zero coding to production AI engineer in eight weeks is not being honest. Good courses state prerequisites clearly (usually Python and basic API knowledge) and offer a foundation track if you are not there yet.

9. What exactly do the placement claims mean?

Be very careful here. "100% placement support" or "100% placement assistance" usually means help with your resume and access to job postings, not a job. Ask:

• How many learners from the last batch got jobs, and in which roles?
• Can I speak to a past learner?
• What exactly does placement support include?

No honest training provider can guarantee you a job. Prefer courses that promise what they can deliver: a strong portfolio, interview preparation and mock interviews.

10. Is the fee fair for what you get?

Rough guide to what different price points usually buy:

• Very low cost (a few hundred to a few thousand rupees): recorded videos, no feedback
• Mid range: live classes, labs, some mentorship
• Premium (₹1 lakh+): longer programs, often university-branded certificates

Compare live hours, code review, project depth and support, not just the price tag or the certificate logo.

Questions to ask before you pay

Copy these into your enquiry:

1. What will I have built and deployed by the end?
2. How is evaluation of RAG systems and agents taught?
3. Who reviews my code, and how often?
4. What are the live hours per week, and when?
5. What happens if I miss classes?
6. What did learners from the last batch go on to do?
7. What does the fee include, and are there payment options?

A good course will answer all of these clearly.

Free resources to start with

Before paying for anything, try learning the basics free to see if you enjoy it:

• Our free Generative AI tutorials and Python tutorials
• Our guides: What is RAG?, What is MCP? and the LangGraph tutorial
• The full agentic AI roadmap

If you finish those and want structure, feedback and a deployed project, that is when a paid course earns its fee.