40h
Get ready to start the bootcamp
Our course is very intense. To be prepared and make the most out of the bootcamp, you must complete 40 hours of preparation work to give you basic tech foundations and culture.
What you will do in practice:
- 40 hours of online tutorials
- Utilize curated resources from Le Wagon to gain solid foundations (Data modeling, programming logic, HTTP and APIs, LLM fundamentals)
40h
Framing the right product to build
Validate a real product opportunity, map the business process and the people around it, and decide whether AI is the right tool or not. Turn discovery insights into a focused MVP scope, roadmap and value proposition.
What You'll Build
- A validated project charter, with your AI-vs-deterministic call documented and defensible
- A stakeholder map and business-process map
- A value proposition, resource plan, success indicators, and MVP roadmap
40h
Design the experience and model its data
Design accessible user journeys, personas and clickable prototypes, then test them with real users. Model the entities, relationships and data your product runs on, with security and GDPR built in from the start.
What You'll Build
- A tested, mid-fidelity clickable prototype with personas and accessibility considerations applied from the design stage.
- An entity-relationship diagram and a technical data-architecture brief
- The first version of your GDPR record of processing activities, plus an eco-design note on your app's footprint
48h
Build your app with no-code
Turn your prototype into a working, deployed application built with a no-code builder and wired to a live data model. Implement business rules, roles and permissions, test across devices, and debug systematically.
What You'll Build
- A first deployed version of the app connected to your live data model
- A documented business rules library
- An access-control matrix verified with test accounts
8h
Automate your product and connect it to anything
Design automation workflows with triggers, conditions, loops and error handling to remove manual work. Connect third-party APIs with authentication, webhooks and retries, and map data cleanly between tools.
What You'll Build
- A set of documented automation workflows, including one complex flow with conditional branches, a loop and error handling
- A working authenticated third-party API integration with error/retry handling and a received webhook
- A documented data-mapping table for incoming and outgoing flows
56h
Build AI that takes action
Build generative-AI features with structured outputs, RAG and agents that act inside your product. Add guardrails, human fallbacks and rate limits, and learn to judge AI on quality, cost, latency and safety.
What You'll Build
- An AI-powered feature integrated into an automation workflow.
- A guided RAG feature and an agent with an appropriately limited set of tools and permissions.
- A cost-per-call and margin estimate, model-selection rationale, and spending cap.
32h
Secure, test and ship to production
Review encryption, backups, logging, permissions and GDPR before going live. Run acceptance tests across security, performance and accessibility, then deploy with a documented release and rollback plan.
What you will do in practice:
- A live application with a security configuration log and a completed GDPR register
- A scenario-based acceptance-testing report covering security, performance and accessibility
- A full AI evaluation scored against a rubric, plus a written deployment procedure with a rollback plan
32h
Run and improve your live product
Diagnose incidents, run first-level support, and read usage data against your product and AI metrics. Run experiments, watch for AI output-quality drift, and prioritise improvements by measurable impact.
What you will do in practice:
- A support runbook with target response times and a user guide
- A usage analysis of your live product
- A prioritised improvement backlog with measurable expected impact, and a one-page launch/GTM note
40h
Work like an AI-augmented Product Manager
Use AI to accelerate discovery, research synthesis, PRD writing, roadmap prioritisation and documentation. Review AI output critically for accuracy, bias and blind spots, and choose when to build no-code, AI-assisted, or a mix.
What you will do in practice:
- A build-approach decision memo: which parts you built No-Code, AI-assisted, or a mix, and why
- A security audit of one screen rebuilt with an AI-assisted-coding tool
- A set of AI-drafted-then-corrected PM artifacts (PRD section, roadmap rationale, meeting minutes, tech-watch note) feeding your capstone
40h
Build and launch your own AI product
The project phase is the ultimate experience of the course. Collaborate efficiently in teams bringing every skill together on a real product you choose: frame it, build it, secure it and launch it live.
What you'll do in practice
- Start from a new opportunity and navigate the same sequence of decisions that product teams face
- Present and defend your finished AI product
- Frame, design, build, launch your final project
