AI-Assisted Engineering

AI-assisted engineering can speed up documentation, code review, triage, testing, and operational analysis. Treat AI tools as helpers inside governed workflows, not as unbounded production actors.

Good use cases

Drafting documentation and runbooks.
Summarizing incidents and pull requests.
Generating test cases from existing behavior.
Explaining unfamiliar code or logs.
Searching internal docs and service catalogs.
Producing first-pass migration plans.

Guardrails

Define rules for:

What data may be sent to AI tools.
How generated code is reviewed.
How suggestions are tested and verified.
Which repositories, systems, or actions tools can access.
How prompts, outputs, and decisions are retained where required.
How model or provider risk is assessed.

Review standard

AI-generated output should meet the same bar as human output.

Code must pass tests and review.
Documentation must be accurate and sourced where needed.
Operational recommendations must be verified against real systems.
Security-sensitive changes need explicit human review.

Automation boundary

Watchouts

Do not paste secrets, PHI, customer data, or regulated data into tools that are not approved for that data.
Do not let AI bypass code review, access review, or change control.
Do not trust generated citations or commands without verification.
Do not give broad production access to autonomous tools without a security model and kill switch.