What is an evaluation rig for AI systems?

An evaluation rig is a repeatable harness that runs the same tasks against a model/prompt and stores outputs and scores so changes can be measured objectively.

How many test cases do I need to start evaluating prompts?

Start with 20–50 representative cases. The key is stability: keep the set fixed so you can compare changes over time.

How often should I run evaluations?

Run evaluations whenever you change prompts, models, retrieval settings, or tool chains. For production workflows, re-run weekly to detect drift.

tools · Article

The baseline evaluation rig

Jan 06, 2025

Disclaimer

This content is provided for educational purposes only and does not constitute professional, legal, financial, or technical advice. Results may vary, and you should conduct your own research and consult qualified professionals before making decisions.

Why a baseline rig matters

Most of the optimization patterns described on this site assume that you can run the same scenario many times and compare outcomes. A baseline evaluation rig is the thin layer of code that makes this possible.

At minimum, it should let you:

Define small test suites representing your critical tasks.
Run those suites against different prompts or models.
Capture scores and metadata in a way that is easy to diff and visualize.

Operator checklist

Re-run the same task 5–10 times before drawing conclusions.
Change one variable at a time (prompt, model, tool, or retrieval).
Record failures explicitly; they are the fastest route to signal.