Question 1

Which is the best Gen-AI course for developers in 2026?

Accepted Answer

The best Gen-AI course is the one that gets you hired: live teaching, real deployed projects, mock interviews, and an instructor who ships AI in production. Vector 2.0 by Prateek Mishra (TechSimPlus) is built around exactly that — a live, project-led cohort where you build 5 production Gen-AI systems (RAG, AI agents, MCP, A2A, fine-tuning, AWS Bedrock, Azure AI, LLMOps), clear 5 one-on-one mock interviews and present on Demo Day to hiring partners.

Question 2

Is Vector 2.0 a good online Gen-AI course for getting a Gen-AI job?

Accepted Answer

Yes — it is designed for jobs, not certificates. You finish with 5 production projects on your GitHub (including TokenGrid, a platform designed for 10 million requests a day), 5 mock interviews with written feedback, a rewritten resume, a polished LinkedIn and GitHub, and job referrals when openings cross our network. No course can guarantee a job, but Vector 2.0 makes you interview-ready for AI Engineer, Gen-AI Developer, Agentic AI Engineer and LLM Engineer roles.

Question 3

Can I become a Gen-AI developer in 3 months?

Accepted Answer

Yes, if you can already write Python and commit about 20 hours a week. Vector 2.0 runs for 2.5 – 3 months across eight sprints: FastAPI, LLMs and LangChain, production RAG, LangGraph workflows, AI agents with MCP and A2A, fine-tuning with AWS Bedrock and Azure AI, LLMOps, and a final capstone with interview prep.

Question 4

How is Vector 2.0 different from other Gen-AI courses?

Accepted Answer

Most Gen-AI courses are pre-recorded and stop at notebooks and LangChain basics. Vector 2.0 is live, capped at 150 developers, and goes all the way to production: deployment on AWS or Azure, scaling to millions of users with gateways, caching and circuit breakers, agent security, and evaluation in CI — taught by a working Sr. Gen-AI Developer, with 5 mock interviews and a Demo Day.

Question 5

Do I need to know LLMs, FastAPI, LangChain, RAG or AI agents before joining?

Accepted Answer

No. You only need Python, Docker and Git — and the pre-work covers all three. Sprint 0 teaches FastAPI from scratch, Sprint 1 teaches LLMs and LangChain from the basics, and every topic after that builds step by step.

Question 6

What does the pre-work cover?

Accepted Answer

Only Python, Docker and Git. Nothing else. Materials are shared within 24 hours of enrolment.

Question 7

What's new in Vector 2.0 compared to Vector 1.0?

Accepted Answer

Vector 2.0 is rebuilt around what companies hire for in 2026: running Gen-AI in production. It now has eight sprints — a new FastAPI foundation sprint, LLMs & LangChain, production RAG, LangGraph workflows, AI agents with MCP and A2A, fine-tuning with AWS Bedrock and Azure AI, and LLMOps for millions of users — plus decision models, five brand-new projects and a Demo Day with hiring partners.

Question 8

Is there an early-bird discount?

Accepted Answer

Yes. Register on or before 30 Oct 2026 and you get 20% off — you pay ₹16,800 instead of ₹21,000. From the next day until registration closes on 11 Nov 2026, the regular fee of ₹21,000 applies.

Question 9

When does the cohort start, and how long does it run?

Accepted Answer

It starts on 21 Nov 2026 and runs for 2.5 – 3 months. Registration closes on 11 Nov 2026, or earlier if all 150 seats fill up.

Question 10

What are the class timings?

Accepted Answer

Tuesday and Thursday for 2 hours (6:30 – 8:30 AM or 8:00 – 10:00 PM IST), plus Saturday and Sunday, 10:00 AM – 4:00 PM IST.

Question 11

What if I miss a live session?

Accepted Answer

Every session is recorded and available within 24 hours. Post your questions in Discord and we'll help you catch up.

Question 12

I'm already working in Gen-AI. Will this be too basic?

Accepted Answer

Unlikely. Sprints 0 and 1 move quickly for experienced learners, and Sprints 4–6 cover areas most working engineers haven't built end to end: MCP and A2A in production, agent security, fine-tuning, AWS Bedrock, Azure AI, vLLM serving, Kubernetes, circuit breakers, and scaling to millions of requests.

Question 13

AWS or Azure — which track should I choose?

Accepted Answer

Everyone learns both AWS Bedrock and Azure AI in Sprint 5. For the deployment work in Sprint 6, pick the cloud your target companies use. Many IT services firms and enterprise GCCs in India use Azure; many product companies and startups use AWS. Both tracks deploy the same flagship project.

Question 14

Do I need a GPU?

Accepted Answer

No. Local work uses Ollama on your laptop, and GPU work for fine-tuning and vLLM runs on free or low-cost notebook GPUs or short rented sessions. We share a tested setup before Sprint 5. You'll need a laptop with at least 8 GB RAM (16 GB recommended) and 50 GB free storage.

Question 15

Will cloud and API usage cost me money?

Accepted Answer

Some, but we keep it low. Most work runs locally with Docker and a local Kubernetes cluster, load tests use a mock LLM so they cost almost nothing, and AWS / Azure free tiers and trial credits cover most of the cloud work from Sprint 5 onwards. We guide you through setup, cost controls and tearing down resources after each session.

Question 16

How much time do I actually need to commit per week?

Accepted Answer

~16 hours of live sessions + 4–6 hours of personal building — plan for about 22 hours per week.

Question 17

Will I get a job after this cohort?

Accepted Answer

The cohort is designed to make you interview-ready and hireable. No course can guarantee a job — anyone who guarantees one is lying. What we ensure: you'll have 5 production projects, 5 mock interview reps, and the skills to defend your work in any senior round.

Question 18

You teach decision models like Jev — isn't that too new to bet on?

Accepted Answer

The pattern matters more than the vendor. Splitting fast, bounded decisions away from generative reasoning is where production Gen-AI is heading, and it's exactly what interviewers probe when they ask about cost and latency. You'll build every decision step with an LLM fallback path, so your system works whether or not you have access to any particular decision model — and you'll compare all three approaches with your own numbers.

Question 19

Will the recordings stay accessible forever?

Accepted Answer

Yes. Lifetime access, including future updates as we refresh content for new models and patterns.

Question 20

Can I get an invoice for company reimbursement?

Accepted Answer

Yes. We provide GST invoices for all enrolments.

Question 21

Is the certificate recognised?

Accepted Answer

The certificate is signed by your instructor. It's a portfolio asset, not an accreditation. What gets you hired is the projects on your GitHub — and we're honest about that.

Question 22

What if I'm not based in India?

Accepted Answer

Welcome. Sessions are in English, IST timings work reasonably for the Middle East and Southeast Asia, and recordings cover the rest.

Question 23

What if I fall behind?

Accepted Answer

Catch up with recordings, get help from the Discord community, and join weekly office hours for targeted support.

Question 24

Do you teach in Hindi?

Accepted Answer

Sessions are conducted in English. Hindi explanations are available on request for any concept that doesn't land.

Question 25

Who is Prateek Mishra?

Accepted Answer

Prateek Mishra is a Sr. Gen-AI Developer and the founder of TechSimPlus, with 8+ years of experience and 20+ production AI projects. He has taught 12,000+ engineers, trained the Vector 1.0 cohort, and personally teaches Vector 2.0 — every project in the course is something he has architected at work or for clients.

Question 26

Is there a pre-cohort orientation?

Accepted Answer

Yes. After enrolment, you receive pre-work within 24 hours and an invitation to a kickoff call held a few days before Day 1.

Prateek Mishra

About Prateek

What Prateek teaches

Vector 2.0 — The Production Gen-AI Developer Cohort

Projects students build with Prateek