AI with Engineering

Combining AI and engineering discipline for speed and maintainability.

AI with Engineering

We combine AI with engineering in a balanced way: AI accelerates exploration and implementation; engineering discipline ensures maintainability and safety.

AI with Engineering

We combine AI with solid engineering: workflows, versioning, and integration so that AI augments delivery without compromising quality.

WorkflowsEnd-to-end automation
IntegrationTools and pipelines
QualityReviews and gates

Where AI helps

Boilerplate, tests, docs, repetitive refactors, and first drafts. We use AI to go faster and to explore options.

Example flow:

  1. You — “Add a unit test for parsePageSize covering valid, zero, negative, and non-numeric. Use our Jest setup.”
  2. AI — Generates a first draft of the test file.
  3. You — Run tests, fix edge cases or style, commit. You own the final code.

We don’t paste AI output straight into main; we treat it as a draft and then apply our standards.

Where humans lead

Architecture, security-critical code, production decisions, and when the problem is ambiguous or high-stakes.

AI can draftHuman decides or implements
Test cases, stub componentsArchitecture and module boundaries
Doc sections, commentsSecurity-sensitive logic and secrets
Refactor of a single functionCross-cutting changes and migrations
Explaining a dependencyWhether to upgrade or replace it

Good collaboration pattern

A pattern that works well: narrow prompt → AI draft → human edit → review.

1. You: Narrow, concrete prompt (see Talent Prompting).
2. AI: First draft (code, test, or doc).
3. You: Run it, fix edge cases, align with our style, add a short comment if needed.
4. Review: Same bar as any other PR; reviewer doesn’t care whether it was AI-assisted.

This keeps quality high while still getting speed from AI.

Feedback loop

We capture what works (prompts, tools, workflows) and feed it back into our patterns and docs so the whole team benefits.

  • If a prompt pattern yields great results, we add it to the Talent Prompting examples.
  • If a tool or setting causes issues, we document the gotcha and the fix.

Balance

AI augments; it does not replace. We keep the feedback loop tight so we improve both our tools and our judgment.

See also