How the work gets done
Good engineering shows up in everyday decisions: how we design an interaction, review a change, test a service and release it. These principles guide our work across software products, AI systems and the platforms behind them.
Understand the people using the system, their workflow and the outcome they need. Explore the service journey, test design assumptions with prototypes and agree the constraints before committing to implementation. Use AI where it serves the problem.
Agree the expected behaviour and acceptance criteria first. Use test-driven development (TDD): write a failing test, implement the change, then refactor while preserving the tested behaviour. For AI behaviour, establish evaluation cases and scoring criteria before changing models, prompts or retrieval. Combine deterministic checks, LLM-as-judge and human review, calibrating automated scores against expert judgement.
Choose a narrow, meaningful slice of the problem. Put working software in front of people early, learn from its limitations and use that evidence to decide what comes next.
Define permissions, data boundaries and approval points explicitly. Give users enough context to understand an answer and a clear way to intervene when the system needs help.
Monitor application errors, latency and availability against agreed reliability targets. Use logs, metrics and traces to diagnose problems, with clear ownership and recovery procedures. For AI workflows, also track response quality, task success and cost. Turn incidents and user feedback into regression tests and evaluation cases.
Keep the architecture understandable and the delivery process repeatable. Work alongside the team, document important decisions and make future changes easier.
Good engineering creates room for change.
A clear architecture, useful tests and shared understanding make the next decision easier.
