Understand
We discuss how your team uses AI today, the delivery process and the quality, security and review concerns the training needs to address.
AI engineering training for software development teams
DabApps helps developers use coding agents within a professional engineering process. Drawing on more than 15 years of building and maintaining software, we teach practical ways to plan, review and verify AI-assisted work, with developers responsible for every change they ship.
Build a shared engineering practice around AI
AI use often grows unevenly. Some developers build effective personal workflows while others rely on occasional browser chat or autocomplete. Engineering leaders need a common approach to quality, security, architecture and review as adoption grows.
Our practical, discussion-led programme helps software teams agree how coding agents fit into delivery. Developers learn how to give agents useful context, check their work and share repeatable practices, so the team can adopt AI without losing ownership of its software.
THE PROGRAMME
What your team will learn
The programme covers three connected areas: understanding coding agents, using them in everyday engineering work and standardising those practices across a team. We adapt the examples to your tools and codebase, covering implementation, testing, debugging, documentation and technical analysis. Human review and verification remain part of every workflow.
- Choosing where chat, autocomplete and coding agents fit
- Setting goals, constraints, acceptance criteria and project context
- Inspecting and planning before making small, reviewable changes
- Using Git, tests, diffs and manual checks to verify agent work
- Explaining unfamiliar code, debugging and refactoring
- Capturing architecture and team conventions in repository guidance
- Connecting ticket review, implementation, PR review and QA
COMMON STARTING POINTS
When training can help your team
You may be introducing agents for the first time, or already have developers using them effectively. Training gives CTOs, engineering leaders and developers space to compare approaches, identify gaps and agree what good practice looks like. Your existing delivery process, conventions and concerns shape the programme.
- Introducing coding agents to the engineering team
- Sharing useful practices beyond a few experienced individuals
- Agreeing how AI-assisted changes should be reviewed and tested
- Making repository instructions and recurring workflows consistent
How the training works
Shape
We adapt the examples and depth to your tools, codebase and experience, agreeing the outcomes that matter to your team.
Practise
Developers work through realistic engineering examples and live demonstrations, with room to discuss questions from their own projects.
Apply
The team puts the ideas into practice between sessions and uses those experiences to refine shared guidance, review habits and handovers.
WHY DABAPPS
Engineering experience behind the guidance
DabApps uses coding agents in its own engineering practice for code review, testing, debugging, refactoring and architecture exploration. Our experience building and maintaining complex software connects the training to real delivery decisions: how to keep changes understandable, respect service boundaries and avoid adding technical debt.
- Guidance grounded in day-to-day software delivery
- A shared approach to agent use across the engineering team
- Smaller changes that developers can understand and review
- Tests and manual verification built into AI-assisted work
- Transferable practices as tools and models change
AFTER THE SESSIONS
Practical outputs your team can keep using
The core programme has been delivered across three connected sessions, giving developers time to apply the ideas between discussions. We adapt the structure and depth to your team's needs. Outputs can include shared checklists, handover templates and repository instructions that capture how your team works and can evolve with the tools.
- AI-assisted workflow checklist
- PR and QA handover template
- Repository guidance for architecture and team conventions
- Starter skills for recurring engineering tasks
Your trainer: Jamie Matthews
Jamie is DabApps’ CTO & Co-Founder and has worked in software development for more than 20 years. An experienced conference speaker and trainer, he focuses on codebase structure, efficient development practices and the day-to-day experience of engineering teams.
His writing on predictable codebases and software architecture explores how clear conventions help developers and coding agents work together. The training brings those ideas into your team's review, testing and delivery practices. For broader questions about AI in your organisation or products, explore our AI consultancy.
Questions about AI engineering training
Who is the training for?
It is designed for software development teams and the CTOs, engineering leaders and development managers responsible for how they work. It suits teams starting to adopt AI as well as teams that already have a few experienced individual users.
Does it depend on a particular programming language or AI tool?
No. The principles apply across languages, codebases, editors and AI vendors. We can demonstrate modern coding agents such as Codex or Claude Code, while keeping the programme focused on transferable engineering practices.
Is it suitable for teams already using ChatGPT, Claude Code or other coding agents?
Yes. Existing experience gives us useful material to work with. We help the team compare current approaches, identify gaps and agree practices that work beyond individual preferences.
Can it fit our existing development workflow?
Yes. We use your delivery process, tools, conventions and concerns as inputs. The aim is to strengthen the way your team already works, with practical changes that can be adopted and reviewed over time.
Is the course practical or theoretical?
It is practical and discussion-led. Sessions use realistic engineering examples and demonstrations, with space to examine questions from your own projects, codebases and development lifecycle.
Engineering ideas behind the training
Jamie Matthews on predictable codebases and the architecture conventions that help developers and coding agents work with them.
