AI Product Builder

How the path is built

An AI feature is still a web feature: it calls a model's API, reads your data and has to hold up in production. So the path lays the technical foundations first, then what the model can do, before designing the feature and proving it is ready.

1. Stage 1

   Technical foundations

   Understand how your product works from browser to server, read and test an API, then model and query the data of your feature. You leave with an annotated diagram of your product, a tested integration spec and ten SQL queries.

   Explain how a web product works, from browser to server · Design and test an API integration as a PM · Model your product’s data and query it with SQL

2. Stage 2

   Understand and steer the model

   Know what an LLM does well and what it misses, write instructions that produce usable results, then get outputs your product can read without guessing. You leave with a delegate, check, keep grid, tested prompts and the output contract of your feature.

   Understand what LLMs do well, and where they fail · Prompting for Product Managers: delegate, describe, verify · Get reliable, product-ready AI outputs (JSON, schemas, function calling)

3. Stage 3

   Design the feature

   Design the assistant end to end, decide whether it needs RAG and specify it, then design an agent and its tools when it is worth the cost. You leave with a design file, a RAG architecture with thirty test questions and an agent file.

   Build an AI assistant for your product · Design a RAG architecture that fits your product · Design reliable agents and tool calling

4. Stage 4

   Prove, secure, ship

   Decide on evidence that it is ready (test set, metrics, thresholds), secure it against prompt injection and assess it under the AI Act, then roll it out in stages and monitor it. You leave with an evaluation plan, a security and compliance file, a dashboard and a model change procedure.

   Evaluate an AI feature: test sets, metrics and LLM judges · Secure an AI product: prompt injection, guardrails and the AI Act · Ship and monitor an AI feature in production

The 12 courses, in order

Each course has its own page: detailed syllabus, prerequisites and deliverable.

1. 1

   Explain how a web product works, from browser to server

   All levels2 hr 30 min

   Follow a request from URL to database, read the Network tab, place an incident in the right layer, and map your own product.

2. 2

   Design and test an API integration as a PM

   Junior3 hr

   Read an API’s docs, test it yourself in Bruno or Postman, and write an integration spec developers can estimate.

3. 3

   Model your product’s data and query it with SQL

   Junior3 hr 30 min

   Read and design a feature’s data model, then answer your own product questions in SQL, read-only, with AI-drafted SQL you know how to check.

4. 4

   Understand what LLMs do well, and where they fail

   All levels2 hr

   Decide, task by task, what to delegate to an LLM, what to verify and what to keep, based on an accurate picture of how it works.

5. 5

   Prompting for Product Managers: delegate, describe, verify

   Junior2 hr 45 min

   Make AI a reliable tool for your PM deliverables: what to delegate, how to describe, how to verify, then an assistant set up for recurring tasks.

6. 6

   Get reliable, product-ready AI outputs (JSON, schemas, function calling)

   Advanced2 hr 30 min

   Turn a model’s answer into data the product can read without guessing: choose the mechanism, design the schema, plan for failures, version the contract, test it on 20 cases.

7. 7

   Build an AI assistant for your product

   Advanced3 hr

   From idea to deployment of an AI assistant that addresses your users’ needs, step by step.

8. 8

   Design a RAG architecture that fits your product

   Advanced3 hr 30 min

   Decide whether you need RAG, then spec each stage: sources, chunking, retrieval, access rights, freshness, cited answers and cost, with 30 test questions.

9. 9

   Design reliable agents and tool calling

   Expert3 hr 30 min

   Decide when an agent is worth it, then design tools, the loop, human approvals, injection defences, traces and evaluation.

10. 10

    Evaluate an AI feature: test sets, metrics and LLM judges

    Advanced3 hr

    Decide on evidence that an AI feature is ready: criteria, versioned test set, grading, calibrated judge, metrics and release gates.

11. 11

    Secure an AI product: prompt injection, guardrails and the AI Act

    Advanced3 hr

    An AI risk register with OWASP, testable layered guardrails, red teaming and incidents, and an AI Act analysis after the Digital Omnibus.

12. 12

    Ship and monitor an AI feature in production

    Expert3 hr

    Launch an AI feature in stages, then know at any time what it costs, how long it takes and whether it answers well.

What buying the path gives you

• Access to the courses the path holds on the purchase date; courses added to the path later are not included.
• Lifetime access: for as long as Module operates, with updates to the content of these courses.
• A certificate for every course you finish.
• One payment via Stripe, no subscription, with an invoice.

Details in the terms of sale (sections 5 and 6)

Other learning paths