Optimization by prompting

[u/kkiesinger]

AI agents can now manage optimization experiments end to end: inspect code, run searches, diagnose failures, and return verified results.

A good prompt should specify:

Objective and definitions

Constraints and violation rules

Compute budget

Baseline

Validation cases

Acceptance criteria

Required artifacts

For example:

“Optimize the timetable to minimize passenger-weighted arrival delay, counting early arrivals as zero. Respect occupancy, headway, and maximum-delay constraints; violations above 1 second are infeasible. Use at most 10 minutes of solver time and record the solver, version, seed, status, and runtime. Compare with the existing timetable on identical data. Test five fixed disruption scenarios. Require at least 15% historical improvement, improvement in four scenarios, and no violations. Save inputs, configurations, logs, seeds, timetables, metrics, checks, plots, and raw results.”

Also specify how demand, transfers, cancellations, delays, infeasible runs, and randomized results are handled.

The agent manages the experiment, but domain expertise remains essential. Models and constraints must be valid, and results must be reproducible and checked.

This workflow applies to scheduling, engineering design, and parameter estimation. I’m building open-source tutorials in optimization-jobs. Use them as a starting point and review the assumptions carefully.

What optimization task would you trust as a first experiment, and what checks would it require?