Product Fluency learning library
Product judgment, technology, data and AI.
Reference
Track 01 · 15 lessons
Product Judgment.
Find the real problem. Choose what is worth solving.
Problem vs Solution
Find the need underneath “we need search.”
Scroll lessonWho Is the User?
Separate the person using the product from the people buying and governing it.
Scroll lessonUser Needs vs Feature Requests
Treat the requested feature as evidence about a larger job.
Scroll lessonJobs to Be Done
Understand the progress someone is trying to make.
Scroll lessonBetter Discovery Questions
Ask about behavior that happened, not approval for your idea.
Scroll lessonAssumptions and Risks
Test the belief that could make good execution irrelevant.
Scroll lessonOpportunity Sizing
Estimate the shape of an opportunity without pretending to know the future.
Scroll lessonRoot Cause vs Symptom
Decompose a metric change before choosing a fix.
Scroll lessonMVP
Build the smallest useful test of the risky assumption.
Scroll lessonScope and Sequencing
Protect the outcome while cutting the implementation.
Scroll lessonPrioritization
Make the reasoning visible before applying a score.
Scroll lessonProduct Trade-offs
Name what you gain, what you give up, and when you would reconsider.
Scroll lessonBuild vs Buy
Decide which capabilities deserve your team's long-term ownership.
Scroll lessonStrategy and Product Bets
Connect today's choices to a coherent way of winning.
Scroll lessonMaking a Recommendation
Write a decision memo that exposes the reasoning and the risk.
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Track 02 · 15 lessons
Technical Fluency.
Follow the system. Ask better engineering questions.
How a Web Product Works
Follow one click from the browser to the database and back.
Scroll lessonFrontend, Backend, Client and Server
Recognize which side of a boundary must change.
Scroll lessonHTTP Requests and Responses
Read enough of a request to ask a precise question.
Scroll lessonAPIs Are Contracts
Turn “the API doesn’t support this” into an actionable discussion.
Scroll lessonJSON and Data Shapes
Read a response without mistaking its shape for its meaning.
Scroll lessonDatabases and Data Models
See why a small feature may require a larger change to the model.
Scroll lessonSQL vs NoSQL
Start with access patterns and constraints, not database fashion.
Scroll lessonAuthentication vs Authorization
Distinguish identity from permission to act.
Scroll lessonCloud, Hosting and Serverless
Understand what your team still owns when infrastructure is managed.
Scroll lessonDNS, HTTPS, CDN and Caching
Explain why a fast site can sometimes show old information.
Scroll lessonGit, Branches and Environments
Follow a change without confusing review, staging, and production.
Scroll lessonCI/CD and Deployments
Understand the path from a code change to a recoverable release.
Scroll lessonAsync Systems
Design for work that finishes after the initial request.
Scroll lessonReliability and Observability
Give engineering evidence that helps diagnose production problems.
Scroll lessonScaling and Architecture Trade-offs
Choose complexity in response to an observed constraint.
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Track 03 · 10 lessons
Data & Experimentation.
Interrogate the numbers before you make the call.
Metrics vs KPIs
Choose the few numbers that should change a decision.
Scroll lessonNorth Star and Input Metrics
Use a shared measure without allowing it to flatten the product's value.
Scroll lessonFunnels
Locate conversion loss without assuming the largest drop is the best opportunity.
Scroll lessonActivation
Find evidence that the user has experienced the product's value.
Scroll lessonRetention and Cohorts
Compare like with like to understand who keeps receiving value.
Scroll lessonInstrumentation
Make sure the event means what the decision assumes it means.
Scroll lessonSegmentation
Check whether a changing mix is telling a false story.
Scroll lessonA/B Testing
Use controlled comparisons while respecting uncertainty and interference.
Scroll lessonData Traps
Challenge a plausible conclusion without getting stuck forever.
Scroll lessonFrom Data to Recommendation
Move from an observation to a bounded product decision.
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Track 04 · 15 lessons
AI Product Management.
Choose where AI helps, and design for where it fails.
What an LLM Actually Does
Understand why useful generation is not the same as verified knowledge.
Scroll lessonTokens, Context and Sampling
Connect model settings to memory, variation, cost, and user expectations.
Scroll lessonChoosing a Model
Select for the task and its failure cost, not the leaderboard.
Scroll lessonPrompt and Instruction Design
Make the intended behavior explicit and test changes like product changes.
Scroll lessonStructured Outputs
Separate predictable format from correct meaning.
Scroll lessonTool Calling
Understand how a model's proposed action becomes a software operation.
Scroll lessonRAG
Use retrieval to supply evidence, then evaluate both finding and answering.
Scroll lessonEmbeddings and Vector Search
Use semantic similarity without confusing it with exactness or truth.
Scroll lessonAgents and Workflows
Choose autonomy only where adapting the next step creates value.
Scroll lessonEvals
Define quality before the demo persuades you the system is ready.
Scroll lessonHallucinations and Uncertainty
Design around the consequences of plausible but unsupported output.
Scroll lessonHuman in the Loop
Put review where a person can actually detect and prevent the important error.
Scroll lessonAI Cost, Latency and UX
Optimize the time and money spent on a successful user outcome.
Scroll lessonAI Safety, Privacy and Data
Ask where information goes and what the system is allowed to do with it.
Scroll lessonWhen NOT to Use AI
Choose the simplest method that can meet the task's correctness requirements.
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Track 05 · 15 lessons
Execution & Communication.
Turn a clear decision into a product that works.
Writing a Strong Problem Brief
Give the team enough shared context to explore useful solutions.
Scroll lessonPRDs That Help
Document the decisions needed to build and validate a coherent product.
Scroll lessonUser Stories and Acceptance Criteria
Describe the benefit, then make the behavior testable.
Scroll lessonFunctional vs Non-Functional Requirements
Specify how well the product must work, not only what it does.
Scroll lessonEdge Cases and Product States
Design the moments when the happy path is unavailable.
Scroll lessonDependencies and Sequencing
Find the chain of work that actually controls the delivery date.
Scroll lessonWorking With Engineering
Ask questions that clarify constraints without pretending to choose the architecture alone.
Scroll lessonWorking With Design
Collaborate on behavior and information, not surface decoration.
Scroll lessonStakeholder Management
Turn competing incentives into explicit decisions and commitments.
Scroll lessonRoadmaps and Communicating Priorities
Explain intended outcomes and the confidence behind timing.
Scroll lessonLaunch Planning
Prepare the system and the people who will carry the release.
Scroll lessonFeature Flags and Rollouts
Separate deploying code from exposing behavior to users.
Scroll lessonIncidents and Retrospectives
Stabilize first, then learn how the system allowed the failure.
Scroll lessonExecutive Communication
Lead with the decision and make the evidence easy to inspect.
Scroll lessonExplaining Technical Trade-offs
Recommend an architecture investment that fits today's evidence and tomorrow's constraints.
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