Lead AI Products
That Actually Ship
8 weeks. 32 live hours. Go from AI-curious to AI-fluent — with the LLM depth, RAG architecture, eval frameworks, and capstone defense that separate junior from senior AI PMs.
What you'll be able to do
Capabilities you can use at work the week after the cohort ends.
Week by week
Every week ends with a deliverable. The capstone runs all 8 weeks — not just the last 3.
Foundations & the AI PM Mindset
- ·AI landscape: predictive vs generative, specialist vs general
- ·ML literacy every PM needs (training, inference, what 'a model' is)
- ·The AI PM mindset — what changes from classical software
Exercise: Explain a familiar AI product's architecture to your partner
The Engine Room: LLMs & Prompt Engineering
- ·How LLMs work: tokens, context windows, temperature, the autoregressive loop
- ·Why models hallucinate — at the architectural level
- ·Prompt engineering: task decomposition, role priming, few-shot, chain-of-thought
Exercise: Build 3 working prompts you'll actually use at work this week
LLM Deep Dive: Lifecycle, ML Collaboration & Tradeoffs
NEW- ·Full model lifecycle: pre-training → fine-tuning → RLHF → eval → deployment
- ·When fine-tuning is right vs. when prompting + RAG is enough
- ·Cost/latency/quality triangle — negotiating with ML teams
Exercise: Write a model selection memo for your capstone
Deciding & Designing: Fitment and RAG
- ·The four-question AI fitment test: when deterministic beats AI
- ·RAG deconstructed: chunking, embedding, retrieval, re-ranking, grounding
- ·Defend your architecture choice in a design review
Exercise: Design a RAG architecture for your capstone — document and defend the chunking choice
Building: AI UX and Agents
- ·AI-native UX patterns: input/output affordances, the trust gradient
- ·Hallucination handling: suppression, disclosure, citation, fallback
- ·Agents demystified: planner-dispatcher, tool use, memory, HITL
Exercise: Build a clickable or coded prototype that demonstrates the AI interaction end-to-end
Measuring: Evals & Reliability
- ·Offline vs online vs online-shadow evals
- ·LLM-as-judge: when it works, when it lies
- ·Guardrails and the eval-driven improvement loop
Exercise: Build a real eval (10–15 cases), run it against your prototype, document failures
Shipping: Launch, ROI & Interview Prep
- ·AI feature cost models: tokens, retrieval, model selection, scale
- ·Launch playbooks: staged rollout, fallback, monitoring
- ·The six AI PM interview question types + mock interviews
Exercise: PRD v2 + ROI/cost model finalized. Demo rehearsed.
Capstone Presentations & Panel Defense
- ·8-minute presentation: problem, fitment, architecture, prototype demo, eval results, ROI
- ·4-minute panel defense: instructor panel challenges every choice
- ·Closing reflection + certificate distribution
Exercise: Final demo + live panel defense
Who is this for?
Built for working PMs who are shipping product and want AI as a real capability.
✓Perfect for
- ✓PMs with 2–8 years experience shipping product
- ✓PMs preparing for AI PM interviews at Senior/Staff level
- ✓PMs inheriting an AI surface area at their company
- ✓Builder-curious PMs comfortable trying new tools
✕Not for
- ✕APMs or aspiring PMs (assumes you know discovery + PRDs)
- ✕PMs looking for a pure theory course — every week has a deliverable
- ✕Senior leaders already running large AI platform teams
What alumni say
Real words from real engineers — not cherry-picked marketing copy.
“I have seen many course creators and cohort instructors who publish their content and then completely disappear when students need help. Yet you guys are answering every question with patience and clarity even when it means explaining the same concept multiple times. Highly appreciate it.”
“Got the correct classification output for a can image. Thanks for nudging me in the right direction. It gives me a solid base to build upon and better understand the concepts practically.”
“Liking the course — it covers required theory + practicals and you are clarifying doubts on time. Usually I have seen people won't respond at all for the queries. Really appreciate it!”
“I joined the Agentic AI cohort to bridge the gap between knowing AI concepts and actually building with them — and it exceeded every expectation. The program covered everything from embeddings and RAG to LangChain, LangGraph, and full agentic workflows, with a strong focus on production-level application. The mentors — Pallav, Karthik, and Ankita — were outstanding. They were patient, knowledgeable, and always helped connect theory to real-world implementation.”
Investment in your PM career
Pricing will be announced shortly. Join the interest list to be notified first.
Pricing Coming Soon
Early interest list members get first access and early bird pricing. Cohort starts July 11, 2026.
Common questions
Everything you want to know before enrolling.
Do I need to know how to code?+
No. You'll understand how AI systems are built without writing production code. You do need to be willing to try things — prompts, evals, lightweight prototyping in tools like v0 and Cursor.
Who is this for?+
PMs with 2–8 years of experience who are shipping today and want AI as a real capability. Also PMs preparing for AI-focused interviews at Senior/Staff/Group PM level.
How is this different from a general PM course?+
This assumes you know discovery, delivery, and how to write a PRD. Every week is about AI products — RAG, agents, evals, LLM lifecycle, and the architecture decisions that separate junior from senior AI PMs.
What is the time commitment?+
5–6 hours/week: 4 hours live (Saturday + Sunday, 2 hours each) + 1 hour pre-read + 30–90 minutes capstone homework.
Are sessions recorded?+
Yes. All sessions are recorded and available for 6 months. Attendance is strongly preferred — the labs don't work async.
What is the capstone?+
Your team designs an AI feature end-to-end across 8 weeks: a problem statement, PRD v1 and v2, a working prototype, a real eval framework, a cost/ROI model, and a live panel defense in Week 8.
Will I get a certificate?+
Yes. A Joinloop certificate on completion, plus access to the alumni Slack channel.