RAG · What interviewers look for — They want a systematic process, not a guess: check the prompt assembly, chunk boundaries, context ordering, and whether the model is synthesising across conflicting passages. Candidates who jump straight to “use a better model” fail this one.

Agents · What interviewers look for — Termination conditions as a design requirement: max steps, max tokens, wall-clock limits, loop detection on repeated tool calls, and a defined escalation path. Bonus points for alerting on near-limit runs, not just hard-stopping them.

System Design · What interviewers look for — The permission part is the trap — access control must be enforced at retrieval time, filtering by the caller’s entitlements before anything reaches the model. They also listen for chunking strategy, hybrid search, reranking, and index sync.

Fundamentals · What interviewers look for — Communication is the real test. Strong answer: RAG injects knowledge, fine-tuning shapes behaviour — and you refuse to fine-tune on facts that change, on tiny noisy datasets, or when nobody can produce a held-out evaluation set.

LLMOps · What interviewers look for — Attribution thinking: per-feature and per-step token tracking, retry loops, prompt or context growth from a recent change, a model routing regression. Candidates who can’t describe their cost dashboard reveal they’ve never run AI in production.

Behavioural · What interviewers look for — Honesty plus engineering maturity. They want a real failure, the detection story, and a systemic fix — usually an evaluation harness or guardrail that now exists because of it. “Nothing has failed” is the worst possible answer.

No ML background needed — if you're comfortable writing Python, you're ready. Week 1 builds agentic foundations from first principles, and several students have come in as pure software engineers and shipped production agents by week 8.

Every session is recorded and you get lifetime access to all recordings. You can also post questions in the private community and get them answered in the next session's Q&A.

Live sessions run every Tuesday, Thursday and Saturday evening (IST). Exact timings are shared with the cohort after enrollment. If you're outside India, recordings are available within hours of each session.

YouTube teaches you the concepts. The course makes you build: six real enterprise projects, live code reviews of your work, direct mentorship, a capstone deployed to production, and a certificate. It's the difference between watching and shipping.

If the course isn't right for you, email within 7 days of the batch start date and you get a full refund — no questions asked, no forms to fill.

Yes — pay in 2 instalments of ₹7,999 or 3 instalments of ₹5,499. Select your preference in the enrollment form and you'll receive the payment schedule by email.

Yes. Complete the capstone project and you receive a certificate of completion — plus a LinkedIn recommendation from Prabhakar for students who finish the full program.

Yes — 3+ enrollments from the same company get a group discount, and teams of 5+ can book a private cohort with a custom curriculum (RAG, Agents, Azure AI Foundry, fine-tuning), on-site or remote. Use the contact form below for a proposal.