Learn
Learn hub Foundations Prompting SQL & data Visualization RAG Chatbots Prototypes Agents & launch FAANG prep
Weekly practice
01020304050607 08091011121314
AI for TPM & Technical PM · 14 weeks

Go from AI-curious
to AI-credible

Learn the technical foundations PMs actually need — tokens, RAG, SQL, chatbots, agents — then build fluency through weekly hands-on practice.

Start learning → Browse chapters
8
Learn chapters
14
Practice weeks
7
Phases
6–10h
Per week

How it works

Three steps to AI fluency
1
Read the Learn hub in order

8 chapters — Foundations → Agents. Each has definitions, diagrams, and PM mental models. Study at your own pace.

2
Set up the practice environment

Create cloudnote.db and run sample scripts. You'll use it every week for SQL, charts, and RAG labs.

3
Work through weekly folders

Each week: Overview → Readings → Practice → Exam → Lab sheet. Save artifacts to my-submissions/.

Course roadmap — 7 phases
Phase 0 · Week 1

Foundations

Tokens, context windows, inference vs training, hallucination, embeddings, RAG vs fine-tuning.

Wk 01
Phase 1 · Weeks 2–3

Prompting

Prompt anatomy, role constraints, few-shot, chain-of-thought, evals, function calling.

Wk 02Wk 03
Phase 2 · Weeks 4–5

SQL & data

SQL as source of truth, text-to-SQL pipeline, threat model, validation patterns.

Wk 04Wk 05
Phase 3 · Week 6

Visualization

Hypothesis-driven charts, chart type selection, Python plotting, quantitative proof.

Wk 06
Phase 4 · Weeks 7–8

RAG

Full retrieval pipeline: chunk → embed → index → retrieve → generate. Failure modes.

Wk 07Wk 08
Phase 5 · Weeks 9–10

Chatbots

Multi-turn context, streaming UX, logging, rate limits, incident playbook.

Wk 09Wk 10
Phase 6 · Weeks 11–12

Prototypes

Reproducible demos, pinned prompts, token cost budgeting, demo script structure.

Wk 11Wk 12
Phase 7 · Weeks 13–14

Agents & launch

Agent loop, tool calling, human gates, multi-step evals, production launch checklist.

Wk 13Wk 14
Quick links
⚙️
Practice environmentSet up cloudnote.db, SQL drills, RAG corpus, Python labs.
 🗓️
Full course mapAll 14 weeks with phases, themes, and milestones.
 📋
Technical learning planMaster roadmap, phases 0–7, FAANG prep, capstone rubric.
 🎓
Graduate courseAssignments, midterm, rubrics, seminar structure.
 🏁
Week 8 — MidtermRAG failure modes + timed midterm checkpoint.
 🏆
Week 14 — CapstoneIntegration, extras review, final exam.
FAANG interview prep

Questions AI PMs & TPMs get asked

Real question types across Meta, Google, Amazon, Apple, and Microsoft for PM / TPM / APM roles with an AI focus. Grouped by domain.

Foundations & LLM literacy
Explain how a large language model works to a non-technical VP. What are the key constraints a PM should keep in mind?
A user says our AI feature is "hallucinating." Walk me through how you'd investigate and mitigate the problem.
What is a context window and why does it matter for product design? Give an example of a product decision shaped by context limits.
When would you choose RAG over fine-tuning? What signals would push you toward one or the other?
How do tokens relate to cost and latency? If you're building a feature at 10M queries/day, what's your token budget strategy?
Our model gets a 90% accuracy score on our eval set. Is the feature ready to ship? What else would you want to know?
AI product design
Design an AI-powered customer support chatbot for a B2B SaaS product. Walk through the key product decisions, trade-offs, and failure modes.
How would you design an evaluation framework for a generative AI feature before shipping to 1% of users?
You're building a text-to-SQL feature for non-technical employees. What are the top three risks and how do you mitigate them?
A competitor ships an AI feature in 3 weeks. Your team says it'll take 3 months to do it right. How do you navigate this?
How do you handle uncertainty in AI outputs in the UI? Give examples of good and bad UX patterns for this.
You need to add a "confidence score" to AI responses shown to users. How would you design, calibrate, and display it?
Design an AI-assisted code review tool for a team of 5,000 engineers. What are the most important product requirements?
Metrics & data
What metrics would you define to measure the success of an AI search feature? How do you separate model quality from UX quality?
Your AI feature's thumbs-up rate dropped from 78% to 62% overnight. Walk me through your investigation. What queries would you write first?
How would you set up an A/B test for a RAG-powered Q&A feature where responses are non-deterministic?
Define a north star metric for a Copilot-style AI writing assistant integrated into a document editor.
How do you measure "helpfulness" for an AI assistant? What's the difference between user satisfaction and task completion?
Token costs grew 4× while DAU grew 2×. How do you diagnose this? What product changes could fix it without degrading quality?
Technical depth (TPM-specific)
Walk me through the architecture of a RAG system. Where are the failure points and what would you instrument to detect them?
Your model latency p99 spiked from 1.8s to 8s in production. How do you triage this? What's your rollback plan?
How does vector search work? How would you choose between Pinecone, pgvector, and Weaviate for a new product?
A team proposes fine-tuning GPT-4o on proprietary data. As TPM, what questions do you ask before approving the project plan?
How do you prevent prompt injection attacks in a product where users can input arbitrary text that reaches the LLM?
Explain the trade-offs between streaming and non-streaming API responses for a customer-facing chat product.
How would you structure a rate-limiting system for an AI API feature that has both free and paid tiers?
Strategy & prioritization
Your team has 3 AI feature proposals: a chatbot, a recommendation engine, and a text-to-SQL tool. How do you prioritize? What framework do you use?
Leadership wants to ship an AI feature in 6 weeks. Engineering says the eval infrastructure alone takes 4 weeks. How do you resolve this?
How do you balance "move fast with AI" against safety, accuracy, and compliance requirements in a regulated industry?
When is it the right time to build AI in-house vs use a third-party API like OpenAI or Anthropic? What factors change that decision?
A major LLM provider suddenly raises API prices by 300%. How does this affect your roadmap? What's your contingency plan?
Behavioral (AI context)
Tell me about a time you shipped an AI feature that didn't perform as expected. What happened, what did you learn, and what did you change?
Describe a situation where you had to explain a complex AI/ML limitation to a skeptical stakeholder. How did you handle it?
Tell me about a time you used data to kill an AI feature idea that was popular internally. How did you build the case?
Give an example of a time you pushed back on engineering's technical approach to an AI problem. What was your reasoning and what was the outcome?
Describe a time you had to make a ship/no-ship decision on an AI feature with incomplete data. How did you frame the risk?