PM Learning Hub

AI features fail in ways A/B tests miss. What evals are, three grading methods compared, and how PMs set the pass rate that gates a release.

Prompt engineering tunes one request. Context engineering curates everything the model sees: workspaces, memory, agent files. What to put in, what to cut, and who owns it.

AI prototyping tools can shorten the time needed to build a working prototype. The saving depends on its scope and complexity. The workflow, what the prototype must still carry, and where a written PRD still wins.

An AI feature can be used daily and fail every task. The outcome metrics that replace raw engagement, the definition games inside each number, and the checks that catch inflation.

Across 12 published comparisons, synthetic users scored 9 encouraging findings against 14 discouraging. Where AI participants help, where they mislead, and the protocol for using them before real research.

Build a PM portfolio with working prototypes, a product teardown, and a decision document. Includes a flexible 90-day plan.

Agree with engineering on who builds prototypes, who owns production code, and how PM-written code gets reviewed.

Every AI feature carries real marginal cost. How to build a cost-per-interaction model, which levers cut it, and where the cost lands in your LTV math.

Compare five ways to price AI products, including seats, usage, credits, and outcomes, against customer value and the cost to serve.

Use a three-horizon roadmap and record the assumptions that could invalidate each bet as model capabilities change.

A practical guide to the New Product Vitality Index for product, strategy, and finance teams who need one number for innovation.

Use AI to organize user-feedback themes, trace claims to source quotes, and review the output before sharing it.

A practical guide to ClipDrop, a macOS command-line tool for saving clipboard content, transcribing audio, and collecting notes.

Ask questions about your data in plain English, check the analysis, and work within the model’s context limits.

How AI answers change discovery, what publishers can do, and why citations still need to be measured alongside traffic and conversions.

Write clear prompts for product work, supply evidence, and check the results. Includes five templates and guidance on tool use.

Find the steps in your funnel where users drop, then test fixes with a clear measurement plan.

DAU/MAU shows how often monthly active users return daily. Define active use consistently and interpret the ratio in context.

Calculate MRR correctly. Track the five components. Diagnose growth gaps with the MRR waterfall. With 2026 benchmarks.

Use cohort analysis and retention curves to find when users stop returning, compare segments, and plan research into the causes.

A practical approach to competitive analysis that informs product, positioning, and sales decisions.

Estimate customer value, choose a pricing model, build tiers, and test price changes with care.

The 40% test, retention curves, and growth accounting. PMF is a spectrum, not a switch. Here's how to know where you stand.

7 practical tips for product managers to configure and use Claude Cowork effectively, covering context files, planning rules, parallel sub-agents, and scheduled automation.

The limits of the skateboard-to-car MVP metaphor and other ways to plan discovery and delivery.

Complete glossary of product management metrics with definitions, formulas, benchmarks, and links to free calculators

Published SaaS comparisons for retention, LTV:CAC, NPS, revenue growth, and CAC payback, with source scope and measurement limits

Compare RICE, ICE, Weighted Scoring, the Impact/Effort Matrix, and the Kano Model by their inputs and trade-offs.

Side-by-side comparison of RICE and ICE scoring frameworks with decision criteria, examples, and when to use each for product prioritization

Learn the 50+ PM terms you'll hear in your first three months. Clear definitions, real examples, and the context that matters.

Calculate customer acquisition cost, lifetime value, the LTV:CAC ratio, and payback period to evaluate unit economics.

Write clear user stories with a common format, testable acceptance criteria, and examples of mistakes to avoid.

Work through competing priorities, conflicting opinions, and organizational politics.

Learn to deploy features safely with feature flags and progressive rollouts. Master risk mitigation while maintaining deployment velocity.

Learn how sample size, significance, and power affect the design and interpretation of A/B tests.

Learn which agile metrics help you assess team performance, which can mislead, and how to start using them. Includes explanations and examples.

Learn the difference between cycle time and lead time, how to calculate both, and how flow efficiency can reveal waiting states.

Learn what technical debt really means and how to fix it without drowning in jargon.

Learn to measure and forecast team velocity for roadmap planning. The guide explains how to improve predictability and communicate delivery dates in plain English.

Learn to write testable hypotheses, define success criteria, and build products through systematic experimentation rather than assumptions.

Test multiple variables together with multivariate testing. Learn factorial design, interaction effects, and when MVT is more useful than sequential A/B tests.

Learn the ICE scoring method for feature prioritization using impact, confidence, and ease.

Use a 2x2 Impact/Effort Matrix to compare ideas by expected impact and effort.

Learn how the Kano Model classifies features by the satisfaction users associate with their presence or absence.

Learn the RICE framework for prioritizing features by reach, impact, confidence, and effort.

Build a scoring system that helps a team compare feature priorities using shared criteria and weights.

Learn how to calculate market size for any product. Master TAM, SAM, and SOM with simple examples and step-by-step instructions.