# Training in product, design, engineering and AI | Tog Lab

> What Tog Lab uses on its own projects becomes training for your team, from service design and design sprints to DevOps, testing and AI-accelerated development. The group practices on a real challenge from your company and leaves with the artifacts built in class.

URL: https://www.toglab.co/en/treinamentos.html

[Português do Brasil](https://www.toglab.co/pt-br/treinamentos.html.md) · [Português de Portugal](https://www.toglab.co/pt-pt/treinamentos.html.md) · [Español](https://www.toglab.co/es/treinamentos.html.md) · [Italiano](https://www.toglab.co/it/treinamentos.html.md)

[Services](https://www.toglab.co/en/servicos.html) Training

# Training in product, design, engineering and AI

[Train my team](https://www.toglab.co/en/contato.html) Build a learning path

## Formats

Choose how your team will learn. Each format can be hired on its own or alongside a project or a staffing engagement, and it goes into the learning path you build just below.

## Build your team’s learning path

Start from a profile or pick topics one by one. The path shows what usually goes together, and the proposal request already includes what you selected.

Start from

Pairs well with

Format

[Request a proposal for this path](https://www.toglab.co/en/contato.html)

## For software houses

Your team learns what Tog Lab uses on its projects and starts offering discovery, design and AI-accelerated development to your own clients.

[Build a program for my company](https://www.toglab.co/en/contato.html)

## AI-accelerated development for companies

The in-company track brings AI-Accelerated into your team: seven modules, in the same order as the stages Tog Lab uses on its projects. In each one, the group produces an artifact that a person approves before the next module.

The same seven stages run on Tog Lab projects. [See AI-accelerated development](https://www.toglab.co/en/ai-accelerated.html)

## How it works

## Let’s design your team’s training

Tell us who will take part and what your company wants to change. Someone from business and someone from engineering on our team will reply within 1 business day.

[Train my team](https://www.toglab.co/en/contato.html) [hello@toglab.co](mailto:hello@toglab.co)

## More on this page

- Design thinking — Understand the problem before choosing the solution.
- Hypothesis workshop — Turn bets into hypotheses you can test.
- Product experimentation — Test with real people before building everything.
- Design sprint — From the question to a tested prototype in a few days.
- Service design — The whole journey, including what happens behind each screen.
- Product design — From the problem to the interface, with clear criteria and method.
- User research — Listen to and observe the people who use it to make better decisions.
- Prototyping and usability testing — Screens you can test the same day.
- DevOps and infrastructure — Predictable deploys, environments and observability.
- Testing and quality — Automated tests, TDD and code review.
- Software architecture — Documented decisions, with cost and trade-offs.
- AI-accelerated development (AI-Accelerated) — Agents across the seven stages, with a person approving each one.
- AI harness and governance — Context, limits and checks so AI can work safely.
- Service as a Software — Rethink the business to deliver finished work with agents.

- Software house
- Engineering team
- Product and design team
- Leadership

- The method that runs on Tog Lab projects — Your team learns the same process we use with clients, instead of a generic course.
- Practice on your own project — The artifacts come from a real challenge at your company and stay with your team.
- New offerings for your clients — Discovery, design sprints, service design and AI-accelerated development become part of what you sell.
- Follow-up after class — Tog Lab supports the first applications and reviews the deliverables with your team.

- In-company training — Design, engineering and AI tracks practiced on your team’s code, processes and challenges. — teams that will apply the method day to day. — Sessions over a few weeks, within the team’s workflow.
- Facilitated workshops — Design sprints, design thinking, hypothesis workshops and product experimentation run with your team. — decisions that need to be made in a few days, with the right people in the room. — One to five days, on site or remote.
- Fractional leadership — A CTO, CPO or Chief AI Officer for a few hours a week to make decisions on architecture, product and AI use. — companies that do not yet need this leadership full time. — A few hours a week, on a fixed schedule, with decisions recorded.
- Talks and lectures — Product, service design and AI engineering at companies, events and universities. — events, leadership meetings and universities. — One session, in person or online, with time for questions.

- Context — Interview the people who know the workflow, let AI organize what was said and validate the rules with the decision makers. — The context document for the chosen workflow (Domain Context), with rules, risks and open decisions.
- Requirements — Ask AI for requirements and acceptance criteria based on the context, and decide what is in and what is out. — A PRD with scope, priorities and acceptance criteria.
- Architecture — Compare the options AI proposes, with cost and trade-offs, and record the decision and the reason. — An architecture decision record.
- Specification — Detail a feature with flows, errors and acceptance criteria in the Given, When, Then format. — A feature specification, ready to be turned into code.
- Prototype — Generate screens from the specification and test them with someone who will use them. — A clickable prototype and a record of the changes made after testing.
- Code and tests — Write the tests before the code, let the agent implement it and review the pull request. — A reviewed pull request, with the tests passing.
- Infrastructure — Turn the deploy into a runbook and set limits on what the agent can execute. — A deploy runbook and the rules for what the agent can do in the repository.

- A conversation about your team — We learn who will take part, how the team already uses AI and what the company wants to change.
- Assessment — A review of the current workflow, the tools and the skill level of each group.
- Tailored program — Together we choose the modules, the format and the company workflow the group will use as its exercise.
- Application and follow-up — The group practices on its own workflow, and Tog Lab supports the first applications after the last class.