Site map

Every public page on Module: main pages, learning paths, courses with their lessons, and legal information.

XML sitemap Summary for AI assistants (llms.txt)

Main pages

Learning paths

Product Manager paths

AI Product Builder

For PMs who design and ship AI features, from how a web product works to monitoring in production. 12 courses

Technical PM

For PMs who want to challenge and work with engineers as peers: web, architecture, APIs, data, authentication, Git. 6 courses

AI-Augmented PM

For PMs who want to speed up discovery, decisions and delivery with AI: prompting, roadmaps, specs, analytics, A/B tests. 5 courses

Go further

Solo Builder

For PMs and founders who want to build an app on their own with an AI coding agent: Git, database, authentication and payments. 4 courses

AI Every Day

The entry point for everyone, managers and non-technical teams included: understand LLMs, choose tools, prompt, use AI at work safely. 5 courses

All courses (31)

AI & Fundamentals

Choosing and using AI tools at work · All levels · 1 hr 40 min
Show the 9 lessons of "Choosing and using AI tools at work"
1. Overview
  1. Course overview
2. Understanding what these tools do
  1. How an AI assistant produces an answer
  2. Fabrications, sources and confidential data
3. Getting a useful first result
  1. A clear request in five elements
4. Choosing the right tool and way of working
  1. General-purpose assistants
  2. Conversation, project, research, agent: choosing the mode
  3. Specialized tools
  4. The tool selection grid
5. Building your toolkit
  1. Testing and settling your toolkit
Prompting for Product Managers: delegate, describe, verify · Junior · 2 hr 45 min
Show the 13 lessons of "Prompting for Product Managers: delegate, describe, verify"
1. Overview
  1. Course overview
2. Delegate, describe, verify
3. Prompting on your PM deliverables
4. Set up an assistant, work with diligence
Understand what LLMs do well, and where they fail · All levels · 2 hr
Show the 8 lessons of "Understand what LLMs do well, and where they fail"
1. Overview
  1. Course overview
2. How an LLM produces an answer
  1. An LLM predicts how a text continues, and that explains almost everything
  2. Hallucinations, sycophancy, variability, the three traps to anticipate
3. What the model knows, and what it sees
  1. What the model knows, since when, and what tools add
  2. The context window, a powerful but uneven working memory
4. Reasoning and multimodality
  1. Reasoning and reading images, what these capabilities really change
5. Deciding what to delegate
  1. The "delegate / verify / keep" grid
  2. Workshop: your grid on ten tasks, the exit kit and the application plan
Use AI at work without exposing your data or your company · All levels · 2 hr
Show the 8 lessons of "Use AI at work without exposing your data or your company"
1. Overview
  1. Course overview
2. What happens to your data
  1. Sorting your information into green, amber and red
  2. What each tool does with your inputs, depending on the plan
3. Personal data
  1. GDPR for AI users, five questions before you paste
  2. Pseudonymise before pasting, and know what it does not do
4. Protecting the company
  1. Secrets, customer commitments, shadow AI and rights over content
5. Transparency and team policy
  1. Saying when AI contributed, and who approves, AI Act included
  2. Workshop: your team's AI policy, the exit kit and the application plan

AI & Product

Build an AI assistant for your product · Advanced · 3 hr
Show the 16 lessons of "Build an AI assistant for your product"
1. Overview
  1. Course overview
2. Frame the assistant
  1. Identify a use case worth an assistant
  2. Write the framing brief
  3. Understand the anatomy of an LLM assistant
3. Design the experience and behavior
  1. Design the conversational experience
  2. Write a production system prompt
  3. Specify the response format for the interface
4. Give it the right knowledge
  1. Manage context and conversation memory
  2. Choose how the assistant gets its knowledge
  3. Specify a RAG system with your team
5. Actions and guardrails
  1. Specify the assistant's tools and actions
  2. Design layered guardrails
6. Evaluate, ship, monitor
  1. Design the evaluation plan
  2. Choose how to build it
  3. Launch and run in production
  4. Assemble the design dossier
Design a RAG architecture that fits your product · Advanced · 3 hr 30 min
Show the 12 lessons of "Design a RAG architecture that fits your product"
1. Overview
  1. Course overview
2. Do you need RAG, and on which sources?
  1. Choose between RAG, long context, injected data and fine-tuning
  2. Inventory sources and metadata
3. Chunk and index
  1. Choose a chunking strategy
  2. Understand embeddings and semantic search
4. Retrieve and answer
  1. Improve retrieval with hybrid search, reranking and context
  2. Specify the cited answer and "not found"
5. Access rights and freshness
  1. Filter by access rights before the model
  2. Keep the index up to date
6. Test, cost and document
Evaluate an AI feature: test sets, metrics and LLM judges · Advanced · 3 hr
Show the 11 lessons of "Evaluate an AI feature: test sets, metrics and LLM judges"
1. Overview
  1. Course overview
2. Why evaluate, and what to measure
  1. Why vibes are not enough
  2. Define measurable success criteria
3. Build the test set
  1. Build a representative test set
  2. Label, version and protect the test set
4. Grade the answers
  1. Choose a grading method per criterion
  2. Design and calibrate an LLM judge
5. Choose the metrics
  1. Read classification metrics
  2. Read RAG metrics
6. Decide on release
  1. Set release thresholds and the regression rule
  2. Assemble your feature's evaluation plan
Ship and monitor an AI feature in production · Expert · 3 hr
Show the 12 lessons of "Ship and monitor an AI feature in production"
1. Overview
  1. Course introduction
2. Launch without betting everything
  1. Launch in stages, with a kill switch
  2. Log and trace without exposing data
3. Measure quality, cost and latency
  1. Track quality in production
  2. Manage cost per request and per outcome
  3. Control perceived latency
4. Incidents, fallbacks and model changes
  1. Recognise AI-specific incidents
  2. Plan the fallbacks
  3. Change models without regressions
5. Tooling and reporting
  1. Choose your observability tools
  2. Report to stakeholders
  3. Final workshop: your dashboard and your procedure
Get reliable, product-ready AI outputs (JSON, schemas, function calling) · Advanced · 2 hr 30 min
Show the 10 lessons of "Get reliable, product-ready AI outputs (JSON, schemas, function calling)"
1. Overview
  1. Course overview
2. From free text to a contract
  1. Diagnose where free text breaks your product
  2. Choose the mechanism that guarantees the structure
3. Design the schema
  1. Write the spec of every field in the schema
  2. Let the model say "I don't know" in the schema
4. When the output breaks the contract
  1. Specify validation, retries and fallback
  2. Plan for the cases where the guarantee does not hold
5. Keep the contract alive
  1. Evolve the contract without breaking the product
  2. Test the contract on 20 cases chosen to break it
6. Your output contract
  1. Assemble the output contract of your feature
Design reliable agents and tool calling · Expert · 3 hr 30 min
Show the 12 lessons of "Design reliable agents and tool calling"
1. Overview
  1. Course overview
2. Agent, workflow or a single call?
  1. Decide when an agent is worth its cost
  2. Choose the right workflow pattern
3. Design tools the model uses well
  1. Write tool cards the model uses well
  2. Specify tool errors and idempotency
4. The agent loop
  1. Specify the loop and its stop conditions
  2. Judge when an orchestrator and subagents are warranted
5. Human control and security
  1. Place human approvals according to blast radius
  2. Counter prompt injection through tool outputs
6. Observe and evaluate an agent
  1. Specify a run trace and its metrics
  2. Evaluate an agent on tasks, trials and trajectories
7. Your agent dossier
  1. Assemble the agent dossier of a real case
Connect AI to your tools and data with MCP · Advanced · 2 hr 30 min
Show the 9 lessons of "Connect AI to your tools and data with MCP"
1. Overview
  1. Course overview
2. What MCP standardizes
  1. What MCP standardizes, and what it does not
  2. Local or remote - transports and authorization
3. MCP or something else?
  1. MCP server, API integration, in-house tool or skill?
4. Find, evaluate and connect a server
  1. Find a server and evaluate it before installing
  2. Connect a server read-only and verify what it exposes
5. The risks specific to MCP
  1. The risks specific to MCP, and the guardrails that hold
6. Expose your product as an MCP server
  1. Exposing your product as an MCP server, a product decision
7. Your MCP decision
  1. Assemble your MCP decision and your connected server
Secure an AI product: prompt injection, guardrails and the AI Act · Advanced · 3 hr
Show the 10 lessons of "Secure an AI product: prompt injection, guardrails and the AI Act"
1. Overview
  1. Course overview
2. Map the threats
  1. Map the threats with OWASP and keep a risk register
  2. Direct and indirect prompt injection, and system prompt leakage
3. Data that leaks, actions that go wrong
  1. Data exfiltration and the lethal trifecta
  2. Improper output handling, excessive agency and unbounded consumption
4. Guardrails and security operations
  1. Specify layered, testable and costed guardrails
  2. Red teaming, abuse monitoring and incident response
5. AI Act and GDPR
  1. Qualify your product under the AI Act: roles, risk levels, timeline
  2. Article 50 transparency and interplay with the GDPR
6. Your register, your guardrails, your analysis
  1. Assemble the risk register, the guardrails and the AI Act analysis

Tech for PMs

Explain how a web product works, from browser to server · All levels · 2 hr 30 min
Show the 12 lessons of "Explain how a web product works, from browser to server"
1. Overview
  1. Course overview
2. From address to page
  1. A request's journey, in six steps
  2. Finding the server and securing the connection (DNS and HTTPS)
3. What the browser and the server say to each other
  1. Reading a request and its response in the Network tab
  2. From received code to displayed page
4. From frontend to database
  1. Frontend, backend and API, who does what
  2. Behind the API, data, files and third-party services
5. Caches, CDN and environments
  1. CDN and caches, fast but sometimes behind
  2. Local, preview, staging, production
6. Diagnose and map your product
  1. Qualifying an incident layer by layer
  2. Drawing a request's journey through your product
  3. Exit kit and application plan
Design and test an API integration as a PM · Junior · 3 hr
Show the 12 lessons of "Design and test an API integration as a PM"
1. Overview
  1. Course overview
2. Reading an API
  1. Reading a REST API's documentation
  2. Status codes, errors and product decisions
3. Testing an API yourself
  1. Sending your first requests in Postman or Bruno
  2. Turning your requests into a tested collection
4. Authentication, permissions and security
  1. API keys, tokens and OAuth 2.0, choosing and specifying
  2. The API flaws a PM should know how to spot
5. An integration that lasts
  1. Pagination and rate limits
  2. Webhooks and idempotency, with no duplicates and no lost events
  3. Sandbox, production, versions and deprecations
6. Writing the integration spec
  1. Writing your product's integration spec
  2. Exit kit and application plan
Model your product’s data and query it with SQL · Junior · 3 hr 30 min
Show the 14 lessons of "Model your product’s data and query it with SQL"
1. Overview
  1. Course overview
2. Read and model data
3. First queries
4. Combine tables
  1. Join tables with INNER JOIN and LEFT JOIN
  2. Join traps, and how to detect them
5. Answer product questions
  1. Break queries down with WITH, compare with window functions
  2. From a product question to a query you can defend
6. Query safely, with AI
Read and challenge a technical architecture · Junior · 3 hr
Show the 12 lessons of "Read and challenge a technical architecture"
1. Overview
  1. Course overview
2. Reading an architecture
  1. Reading an architecture diagram with the C4 model
  2. From vague expectations to measurable requirements
3. Structuring the system
  1. Monolith, modular monolith or microservices
  2. Wait or process later, synchronous and asynchronous
4. Data, load and dependencies
5. Deciding and recording
  1. Framing a trade-off and recording it in an ADR
  2. Technical debt, a product decision
6. Running the architecture review
  1. Running the architecture review of your product
  2. Exit kit and action plan
Design authentication and permissions for a product · Junior · 2 hr 30 min
Show the 10 lessons of "Design authentication and permissions for a product"
1. Overview
  1. Course overview
2. Knowing who signs in
  1. Authentication, authorization and sessions
  2. Passwords and account recovery
  3. Two-factor authentication and passkeys
3. Delegating sign-in
  1. OpenID Connect and signing in with Google, Apple or Microsoft
  2. Enterprise SSO, SAML, OIDC and SCIM provisioning
4. Deciding who can do what
  1. Roles, attributes and the roles × actions matrix
  2. Isolation between customers, invitations, audit and account deletion
5. Specifying and choosing
  1. Specifying the journeys and choosing a provider
  2. Exit kit and action plan

Product Management

Design and Validate a Product Experience with AI · Junior · 2 hr 30 min
Show the 12 lessons of "Design and Validate a Product Experience with AI"
1. Overview
  1. Course overview
2. Understand the problem
  1. Write a testable problem statement
  2. Run and synthesize discovery interviews, with AI
  3. Evidence-based personas and job stories
  4. Choose an opportunity and its riskiest assumptions
3. Design the solution
  1. Map the flow and its edge cases
  2. Wireframes, screen states and heuristic critique
4. Prototype and test
  1. Prototype, including with AI
  2. Run a usability test and prioritize the problems
5. Prioritize, plan, hand off
  1. Prioritize the backlog with RICE and MoSCoW
  2. Build an outcome-based roadmap
  3. Hand off to the development team
Git for PMs: ship as a team without putting production at risk · Advanced · 3 hr 30 min
Show the 13 lessons of "Git for PMs: ship as a team without putting production at risk"
1. Overview
  1. Course overview
2. Understand the repository and its pipeline
  1. A mental model of Git in a team
  2. Read a repository and its pipeline before touching it
3. Work on a branch, day to day
  1. Everyday commands, and what can go wrong
  2. Branching strategy and commit messages
4. Take a pull request all the way to merge
  1. Open a pull request the team wants to review
  2. Review, checks, preview and merge
  3. Resolve a conflict without panicking
5. Safety nets and rollback
  1. The safety nets that protect production
  2. Undo a change without making things worse
6. Coding agent, releases and end-to-end delivery
  1. Put an AI coding agent to work in a team repository
  2. Tags, versions and release notes
  3. Final workshop: ship an improvement end to end
Write specs that developers and AI agents understand · Advanced · 2 hr 30 min
Show the 10 lessons of "Write specs that developers and AI agents understand"
1. Overview
  1. Course introduction
2. The job of a spec
  1. What a spec is for, and at what level to write it
  2. Intent before solution
3. Criteria, edge cases and requirements
  1. Write verifiable Given / When / Then criteria
  2. Edge cases and non-functional requirements
4. Specs for coding agents
5. Keep the spec alive
  1. Keep the spec alive, avoid the anti-patterns
  2. Final workshop: your spec and your intent file
Instrument a product and use data to make decisions · Advanced · 3 hr 30 min
Show the 14 lessons of "Instrument a product and use data to make decisions"
1. Overview
  1. Course overview
2. Choose what to measure
  1. North Star, input metrics and guardrails
  2. From decisions to metrics, and a definition of activation
3. Design the tracking plan
4. Reliable, compliant data
  1. QA and monitor data collection
  2. Collect by the rules, consent, CNIL and minimisation
5. Analyse usage
6. Decide with data
Prioritize and build an outcome-based roadmap · Advanced · 2 hr 30 min
Show the 11 lessons of "Prioritize and build an outcome-based roadmap"
1. Overview
  1. Course overview
2. From outputs to outcomes
  1. Outputs, product outcomes and business outcomes
  2. OKRs that help, and their failure modes
3. Prioritize with method
  1. One method per question: RICE, ICE, Kano, opportunity score
  2. When time costs money: cost of delay, CD3 and WSJF
4. Stakeholders and requests
  1. Handle requests from sales and other stakeholders
  2. Say no, or not now, without closing the door
5. Build and maintain the roadmap
6. Final workshop
  1. Final workshop: your product's outcome-based roadmap
Design and analyze an A/B test · Advanced · 3 hr
Show the 11 lessons of "Design and analyze an A/B test"
1. Overview
  1. Course overview
2. Should you test?
  1. What a controlled test proves, and what an analysis does not
  2. When not to test, and what to do instead
3. Write the protocol
4. Analyze the result
5. Decide and capitalize
  1. Decide by written rules and document the experiment
  2. Final workshop: your experiment's protocol, analysis and memo

AI & Development

Build and ship a web app with an AI coding agent · Junior · 2 hr 45 min
Show the 10 lessons of "Build and ship a web app with an AI coding agent"
1. Overview
  1. Course overview
2. Frame the tool and choose how to build it
  1. Chat, app builder or coding agent?
  2. Frame your tool and its success criteria
3. Set up your machine and generate the first version
  1. Set up your workstation
  2. From framing to plan, then to code
  3. Understand what the agent built
4. Iterate, debug and review
  1. Iterate in small, checked steps
  2. Debug methodically and review before sharing
5. Publish the app and keep it alive
  1. Publish from GitHub with Netlify
  2. Evolve the live app without breaking it
Add a database, authentication and payments to your app · Advanced · 4 hr
Show the 11 lessons of "Add a database, authentication and payments to your app"
1. Overview
  1. Course introduction
2. Decide on the architecture and protect your secrets
  1. What has to leave the browser, and where to put it
  2. Environment variables and secrets, without leaks
3. Store the data and protect access to it
  1. Have the agent create your tables, then check them
  2. Row Level Security: everyone's own data, proven with two accounts
4. Add user accounts
  1. User accounts: sign-up, sign-in, session
5. Take a Stripe payment, in test mode
  1. Open a Stripe Checkout payment from the server
  2. Webhooks: payment status is decided on the server
6. Get compliant, deliver and go live
  1. GDPR and switching to live mode
  2. Workshop: your app with accounts, data and payments
  3. Exit kit and application plan
Work with Claude Code on a real project: context, planning, review · Advanced · 3 hr
Show the 10 lessons of "Work with Claude Code on a real project: context, planning, review"
1. Overview
  1. Course introduction
2. Master context and CLAUDE.md
  1. What Claude Code has in mind, and how to manage it
  2. Write a CLAUDE.md that actually helps
3. Plan, then delegate with method
  1. Explore, plan, code, commit on existing code
  2. Subagents, skills, commands and MCP servers: the right tool for each need
4. Put guardrails in place that hold
  1. Permissions: what the agent may do without asking you
  2. Hooks: rules that always apply
5. Review, automate and deliver your set-up
  1. Review the agent's work, then automate it
  2. Workshop: your way of working on your repository
  3. Exit kit and application plan

AI & Productivity

Build reliable Claude Skills for your product work · Advanced · 2 hr 30 min
Show the 13 lessons of "Build reliable Claude Skills for your product work"
1. Overview
  1. Course overview
2. Understand skills and when to use them
  1. What a skill is, and how Claude loads it
  2. Skill, CLAUDE.md, subagent, MCP or hook?
  3. Anatomy of a SKILL.md
3. Design and write your skill
  1. Write a description that triggers at the right moment
  2. Scope your skill before writing it
  3. Write the SKILL.md and install it
  4. A skill's tools and permissions
4. Test, share and measure
  1. Test a skill before relying on it
  2. Case study: the skill in a real PRD workflow
  3. Choose, audit and share skills
  4. Measure value and help a skill mature
5. Wrap-up
  1. Wrap-up and resources

AI & Business

Put AI to Work in Your Business · All levels · 2 hr 10 min
Show the 13 lessons of "Put AI to Work in Your Business"
1. Overview
  1. Course introduction
2. Understand the tool before you use it
  1. What an AI assistant really does
  2. Decide which data to give to AI
3. The five-step method
  1. Choose the task to hand over and frame the deliverable
  2. The five-step method, end to end
  3. Describe a complete request
  4. Choose the right tool for the deliverable
  5. Check the result and improve it
  6. Workshop: apply the method to your case
4. Capture it and share it with the team
  1. Turn a good result into a reusable workflow
  2. Move to an online deliverable without mistaking a prototype for a product
  3. Write your team's AI policy
  4. Your 30-day plan and resources

AI & Industries

Bring AI into architectural visualisation · Advanced · 2 hr
Show the 8 lessons of "Bring AI into architectural visualisation"
1. Overview
  1. Course overview
2. Where AI fits in the architectural workflow
  1. Locate AI in a project's workflow
3. Produce variations and renders
  1. Generate variations from the 3D model
  2. Renders and post-production with AI
4. From 3D model to textured model
  1. AI-generated materials, objects and context
5. Rights, confidentiality and transparency
  1. Usage rights, copyright and confidentiality
  2. Transparency towards the client, the jury and the public
6. Which chain for which deliverable
  1. One tool chain per deliverable
AI for Real Estate Professionals (France) · Junior · 2 hr 10 min
Show the 12 lessons of "AI for Real Estate Professionals (France)"
1. Overview
  1. Course introduction
2. Frame AI in your work
  1. Map the tasks to hand to AI
  2. Protect your clients' data
3. Model a reliable rental yield
  1. Master the indicators before handing them to AI
  2. Have AI build a yield spreadsheet, then check it
  3. Check tax and regulations without delegating the advice
4. Speed up sales tasks without losing reliability
  1. Write compliant listings with AI
  2. Value from DVF comparables, not from the AI's memory
  3. Qualify leads and prepare follow-ups
5. Equip field prospecting
  1. Specify a field prospecting app
  2. Prototype without code and know the limits
  3. Your toolkit and resources

Learning & Training

Design and launch an effective online course · Junior · 3 hr 15 min
Show the 15 lessons of "Design and launch an effective online course"
1. Overview
  1. Course overview
2. Frame the course
  1. Write the promise of your course
  2. Write observable objectives
  3. Align objectives, assessments and activities
3. Design a course that makes people learn
  1. What research knows about learning
  2. Build the plan of your module
  3. Choose and produce the content
  4. Write questions that make people learn
4. Produce with AI without losing control
  1. Decide what to hand over to AI with the 4Ds
  2. Produce with AI, check, stay transparent
5. Choose a platform and publish by the rules
  1. Choose a platform against explicit criteria
  2. Configure the platform and respect the legal framework
  3. Test with real learners, then launch
6. Measure and improve
  1. Measure what matters and fix the priority first
7. Wrap-up
  1. Wrap-up and resources

Legal information and help

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