How to Choose an AI Course in India: 10 Questions to Ask First

To choose an AI course in India, check ten things before you pay: whether classes are live, who the mentors are, what you will build, whether the syllabus covers RAG, agents, MCP and evaluations, whether your code is reviewed, honest prerequisites, a verifiable certificate, no placement guarantee, refund terms in writing, and a chance to meet a mentor before enrolling.

First, decide what kind of AI you want to learn

"AI course" can mean very different things. Some teach data science and machine learning: statistics, model training and analysis. Others teach AI engineering: building applications on large language models with RAG, agents and tool integrations. Neither is better, but they lead to different jobs. Decide which one you want, then judge courses against that goal, not against the length of their syllabus.

The 10 questions to ask before you enrol

1. Are classes live, recorded, or both?

Live classes let you ask questions while you are stuck; recordings let you revise and catch up. The strongest format offers both. Ask how many hours are live, when the sessions run, and what happens if you miss one.

Red flag: a "live" course that turns out to be pre-recorded videos with an occasional group call.

2. Who are the mentors, and do they build AI for a living?

AI engineering changes quickly, so teachers who ship real systems bring current, practical judgement. Ask who will actually teach your batch, what they work on day to day, and whether you can speak to one before you join.

Red flag: a famous name in the advertising, but unnamed trainers in the classroom.

3. What will I actually build?

Ask for the list of projects and the capstone. Good projects are specific: a RAG service with citations and evaluations, an agent that calls real tools, an API deployed to the cloud. Your projects should live in your own GitHub account, because employers judge what you can show.

Red flag: projects described only as "real-world industry projects" with no details.

4. Is the syllabus current: RAG, agents, MCP and evals?

A current AI engineering syllabus should cover structured outputs, embeddings and vector search, retrieval-augmented generation, tool calling and agents with human approval, the Model Context Protocol (MCP), evaluations and tracing, cost control and deployment. Ask when the syllabus was last updated.

Red flag: a course that is mostly prompting a chatbot, or an older syllabus with "Generative AI" added to the title.

5. Will someone review my code?

Code review is where most learning happens. Ask whether mentors comment on your pull requests, how quickly, and whether your capstone is reviewed by a senior engineer rather than auto-graded.

Red flag: "doubt support" that means a chat group with no one looking at your code.

6. Are the prerequisites stated honestly?

A trustworthy course tells you who it is not for. Building LLM applications requires programming, so a course for complete beginners should start with Python properly, and an advanced course should expect real development experience.

Red flag: a promise to turn a non-coder into an AI engineer in a few weeks.

7. Is the certificate verifiable?

Ask who issues the certificate and whether an employer can check it online with a unique ID. A training certificate is not a university degree, and an honest provider says so. Employers care far more about your portfolio than about the paper.

Red flag: vague claims such as "globally recognised" or "accredited" without saying by whom.

8. What exactly is promised about jobs?

No training provider controls who an employer hires. Ask what support is actually included: resume reviews, mock interviews, introductions within the provider's network. Then ask to see the terms in writing.

Red flag: any "guaranteed placement" promise, or "pay after placement" with conditions you only discover later.

9. Are refund and batch-transfer terms in writing?

Exams, illness and job changes happen. Ask what happens if you need to pause, move to a later batch or leave early, and get the answer in writing before you pay, not as a verbal promise on a sales call.

Red flag: pressure to pay today because "seats are filling fast", with terms shared only afterwards.

10. Can I see a class or talk to a mentor first?

A demo class, a sample recording or a call with a mentor shows you the teaching style before you commit. A good counsellor will also tell you when a different course suits you better.

Red flag: you only ever speak to sales staff who cannot answer technical questions.

Three questions to ask yourself

The course is only half of the decision. Before you compare providers, answer these honestly:

• How many hours a week can I really give? Live classes plus practice take more time than the class hours alone. Pick a schedule, such as weekday evenings or weekends, that you can keep for the whole programme.
• Does my setup work? Check the laptop requirements, and whether labs need LLM API keys or a cloud account and how the course keeps those costs under control.
• Where do I want to be at the end? A first developer job, a move from backend work into AI, or leading AI projects at your current company each point to a different programme.

Applying the same questions to Ezibell AI Academy

Ezibell AI Academy is run by Ezibell Tech in Jaipur and teaches applied AI engineering with LLMs. It does not teach data science or machine learning. Here are our answers, using only what is published on our programme pages:

1. Format: live online classes, with recordings of every session.
2. Mentors: working senior Python developers and team leads who build AI, backend and AWS systems for clients.
3. Projects: listed on every programme page. The AI Engineer Accelerator, for example, includes a production RAG service with an evaluation suite, a text-to-SQL assistant, a LangGraph agent with human approval steps, your own MCP server and a capstone deployed on AWS.
4. Syllabus: the Accelerator covers RAG, LangChain, LangGraph agents, MCP, evals, tracing and guardrails; our generative AI course page summarises the topics.
5. Code reviews: mentors review your GitHub pull requests, and capstones are reviewed by a senior developer or team lead.
6. Prerequisites: the Python to AI Launchpad needs no coding background; the Accelerator expects a year or more as a developer. See the beginners and working professionals pages.
7. Certificate: issued by Ezibell Tech (MSME/Udyam and GST registered) with a unique ID anyone can check on our certificate verification page. It is a professional training certificate, not a university degree or government accreditation.
8. Jobs: no job guarantee. Programmes include portfolio projects and, where listed, resume reviews and mock interviews.
9. Refunds and transfers: terms are shared in writing before you pay. Fees and EMI options are shared on WhatsApp.
10. Before enrolling: a free counselling call with a mentor, who will recommend a different programme if it suits you better.

Next step

Take this checklist to every course you are considering, including ours. To ask us any of these questions directly, WhatsApp the academy at +91 82097 16316.