AI teams say this all the time:
“Let’s try a different prompt or model.”
But AI experimentation isn’t UI A/B testing.
Key differences:
- Changes affect meaning, not layout
- Evaluation requires reasoning, not CTR
- You must test offline before users see results
Prompts × models × parameters create combinatorial chaos
A usable AI experiment pipeline needs:
- Prompt versioning with side-by-side evaluation
- Model comparisons on the same task
- Parameter sweeps that aren’t random
- Multi-axis comparison (quality, cost, latency)
A practical workflow:
Step 1: Build or generate a test set
Step 2: Define variants
Step 3: Run evaluations automatically
Step 4: Compare results clearly
Step 5: Deploy with confidence
If every experiment is a manual effort, teams experiment less.
Infrastructure doesn’t slow you down. It’s what enables speed.
How many meaningful AI experiments did your team run last month?
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