AI Coding Assistants for Engineering Teams
Most teams now have an AI coding tool. Far fewer have a way of working with it.
A short, tool-agnostic programme for engineering teams choosing, rolling out or getting more from AI coding assistants. It covers the main tools and how to evaluate them, when to use completion, chat and agent modes, how to give assistants the right context, how to review AI-generated code, and how to set team standards for security, licensing and disclosure. It ends with a rollout and measurement plan. For deep, single-tool training, see our Claude Code programme.
Curriculum
The AI Coding Assistant Landscape
How tools such as GitHub Copilot, Cursor and Claude Code differ in approach, deployment options, security controls and cost. A structured way to evaluate assistants against your stack, codebase and policies.
Completion, Chat and Agent Modes
What each mode is good at, and where it goes wrong. Choosing the right mode for boilerplate, refactoring, debugging, tests and multi-file changes. Keeping engineers in control when an agent makes changes across a codebase.
Context Engineering
Why assistants perform far better with context: repository instruction files, architecture notes, conventions and examples. Structuring a codebase and its documentation so assistants follow your team's standards.
Reviewing AI-Generated Code
Common failure patterns in AI-written code: plausible but wrong logic, insecure defaults, outdated libraries and missing edge cases. A review checklist, and pairing AI output with tests so problems surface before merge.
Team Standards, Security and Licensing
Setting what code and data may be shared with assistants, handling secrets, understanding licensing and retention settings, and disclosure rules for AI-assisted changes. Turning these decisions into a short, enforceable team policy.
Rollout and Measurement
Designing a pilot, onboarding engineers, and gathering feedback. Useful metrics such as cycle time, review effort and defect rates, and why lines of code is a poor measure. A rollout plan you can take to your engineering leadership.
Learning Outcomes
- ✓Compare the main AI coding assistants and choose the right fit for your team, stack and security needs
- ✓Use completion, chat and agent modes appropriately for different kinds of work
- ✓Give assistants the context they need through repository instructions, documentation and conventions
- ✓Review AI-generated code for correctness, security and maintainability before it merges
- ✓Set team standards for AI-assisted work: what is allowed, how it is reviewed and how it is disclosed
- ✓Protect code and secrets with the right privacy, licensing and access settings
- ✓Measure the effect of AI assistants on delivery with sensible metrics
- ✓Roll out an assistant across a team with a pilot, training and a feedback loop