MotherDuck · Practitioner Series

Stop guessing whether your AI analytics stack works.

Run your first eval this week.

Free PDF. 7-step eval playbook. Works on any warehouse.

The eval in action

We ran the playbook against a public benchmark. Same questions, same agent, one schema change. The lift held across four model families without retuning.

Gemini 3 Flash · Hard questions

Baseline 7.2%

After recipe 44.4%

3.1× the paper’s best published Hard result

Claude Opus 4.7 · Hard questions

Baseline 31.2%

After recipe 59.9%

4.1× the paper’s best published Hard result

7 steps

A complete eval playbook for AI-on-data.

3 dimensions

Accuracy, speed, and cost — measured together.

4 models

Lift held across four model families.

−$30

Cost savings on Opus, with higher accuracy.

You don’t need a better opinion about your AI analytics strategy. You need a way to measure whether it works.

The answers keep coming — semantic layers, RAG, MCP, agents writing SQL. Some are good. Some are expensive theater. Most are impossible to judge from a demo, because the demo isn’t running against your data.

This guide is the eval framework. How to run controlled experiments on your own warehouse, what to measure, where the traps are, and how to turn results into something you can keep improving.

What most teams get wrong

Batching Changes

Five changes in one test means a number you can’t explain. One change per eval run.

Stale Context

More context helps — until it disagrees with your schema. Then it actively hurts. We tested this and the result flipped on us.

Time Drift

Baseline today, intervention three weeks later. The model provider ships a snapshot between them. Now you’re measuring two moving targets.

Swapping the Model First

Model swaps are cheap. Recipe wins compound across model generations. Fix the recipe first.

What’s inside the guide

An eval playbook, a worked example, and a Monday checklist.

The Framework

A 7-step eval playbook

Build your question set. Isolate variables. Train the lever against failure traces. Close the loop with production data. Each step comes with the traps to avoid.

The Worked Example

Column naming as the first lever

A controlled experiment on a public benchmark. Four model families, one schema change. Hard-question accuracy jumped from 7% to 44% on Flash, at lower cost.

The Monday Checklist

What to do Monday

Nine concrete steps to run your first eval. Carve out 25 questions, pin the data, wire the harness, score the baseline. Designed to fit in a week.

The playbook

Seven steps. The first six get you to a real lift number. The seventh keeps it improving.

Step 1

Build the question set

Start with 20–30 questions from someone who knows the business. Keep train and held-out separate. Coverage matters more than count.

Step 2

Stabilize your data

The data has to hold still across re-runs. Version the held-out set alongside the harness.

Step 3

Define the baseline

Run the held-out set against your current setup. Log accuracy, speed, and cost per question.

Step 4

Apply the lever

Pick one variable. Fork the data, apply the change, re-run. Only the lever moves.

Step 5

Train the lever

Read failures, propose fixes, re-run on the train set. Stop when improvement plateaus, then score the held-out set once.

Step 6

Iterate the recipe, not the model

Recipe changes compound across model generations. Model swaps don’t. Fix the recipe first.

Step 7

Close the outer loop

Ship with telemetry. Mine production traces for new eval questions. Re-train when the score drifts.

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