Developers are skeptical by default, and that is a good thing when choosing a Gen-AI course. Here is a technical checklist to score any online GenAI course before you enrol.
The scoring checklist
Give each item 1 point. A strong course should score 10 or more out of 12.
| # | Check | Why it matters |
|---|---|---|
| 1 | Starts with API engineering (FastAPI, Pydantic, async) | Gen-AI ships as APIs |
| 2 | Covers structured output, tool calling, fallbacks, cost control | First topics in Gen-AI interviews |
| 3 | RAG beyond basics: hybrid search, reranking, multi-tenancy | Where real systems break |
| 4 | RAG evaluation in CI (RAGAS, DeepEval) | Separates demos from production |
| 5 | LangGraph with checkpointing and human-in-the-loop | Needed for real workflows |
| 6 | Agents with MCP and A2A, plus guardrails and red-teaming | Appears in 2026 job descriptions |
| 7 | AWS Bedrock or Azure AI hands-on | Enterprises run on these |
| 8 | Fine-tuning with LoRA/QLoRA, and when not to fine-tune | Common senior interview question |
| 9 | LLMOps: vLLM, gateways, caching, rate limits, load tests | Senior-level differentiator |
| 10 | Every project deployed and on your GitHub | Proof beats certificates |
| 11 | One-on-one mock interviews with feedback | Converts skills into offers |
| 12 | Live schedule that fits a full-time job, with recordings | Completion matters |
Red flags
- Syllabus is mostly prompt engineering and no-code tools
- Projects are "chat with PDF" notebooks with no deployment
- No mention of evaluation, observability or security
- Instructor has no visible production work
- Promises of guaranteed jobs or salaries
How Vector 2.0 scores
Full disclosure: I am writing this for TechSimPlus. Vector 2.0, taught by Sr. Gen-AI Developer Prateek Mishra, was designed against this checklist and covers all 12 items:
- Sprint 0: FastAPI foundations, ending with a reusable Launchpad starter kit
- Sprints 1 to 4: LangChain, production RAG, LangGraph, agents with MCP and A2A
- Sprint 5: fine-tuning, AWS Bedrock and Azure AI
- Sprint 6: LLMOps with vLLM, LiteLLM, Redis, Kubernetes and k6 load tests
- Sprint 7: capstone, Demo Day and the final senior mock loop
The stack includes FastAPI, LangChain, LangGraph, Pinecone, Qdrant, pgvector, RAGAS, DeepEval, CrewAI, vLLM. Prerequisites are only Python, Docker and Git.
It does not promise a guaranteed job. It does give you 5 deployed projects, 5 mock interviews, resume and LinkedIn reviews, and referrals when openings come through the network.
Next cohort: 21 Nov 2026, early-bird ₹16,800 till 30 Oct 2026.
Visit: http://vector.techsimplus.com
Use the checklist on any course you are considering, and share your score in the comments.
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