AI Training for Software Engineers
Practical, team-focused AI training for engineering teams using Claude, Cursor and other AI coding tools. We help you get consistency across the team, governance you can actually enforce, and a real productivity lift — including how to design and build agents that work in a team environment, not just impressive demos.
Why engineers need their own AI training
Most AI training is aimed at knowledge workers — writing emails faster, summarising documents, tidying up spreadsheets. That is not the problem an engineering team has. Engineers are shipping code into production, working in shared repos, on-call for the outcome. The stakes and the workflows are different.
When Claude, Cursor and other AI tools land in an engineering team without a shared way of working, you get three predictable problems: inconsistent code style and quality across pull requests, quiet governance risk (IP, licensing, customer data pasted into the wrong place), and a lot of impressive demos but very few agents or workflows that actually run in production. This programme is built to fix all three.
What the programme covers
Six areas we work through with engineering teams — tailored to your stack, your tools and the way your team already ships.
Tool fluency for engineers
Hands-on training across Claude, Cursor, GitHub Copilot and other AI coding assistants — what each is good at, where they fail, and how to switch between them without losing flow.
Consistency across the team
Shared prompts, house rules, project conventions and review patterns so ten engineers using Claude or Cursor produce code that looks like it came from one team — not ten.
Governance and safe use
What is safe to paste into an AI tool, how to handle IP, licensing and customer data, and how to keep human review in the loop on anything that ships to production.
Building agents that actually work
Design patterns for agentic workflows — planning, tool use, memory, evaluation and guardrails — so your team ships useful agents instead of impressive demos.
Productivity, not just code generation
Using AI across the full engineering lifecycle: spec writing, ticket refinement, code review, tests, debugging, docs and incident response — where the real hours are.
AI in a team environment
How AI changes pair programming, PR review, on-call, onboarding and knowledge sharing — and the working agreements that keep quality high as velocity climbs.
Who it's for
- Engineering leaders rolling AI coding tools out to a team or a whole org
- Platform and staff engineers setting standards for how AI is used in the codebase
- Product engineers who want to move faster without breaking review discipline
- AI/ML and applied engineers building internal agents and AI-powered features
Outcomes you can expect
- A shared way of working with Claude, Cursor and other AI tools across your engineering team
- House rules and prompt patterns committed alongside the codebase — not lost in Slack
- Clear guardrails for IP, licensing and customer data when AI touches the repo
- Repeatable patterns for building agents: planning, tool use, memory, evals and safety
- Measurable lift in PR throughput, review quality and time-to-first-commit for new joiners