Case study · May 2026
AI Prompt Playground and Bulk Testing Suite
A shared environment for testing, scoring, and validating AI output before it reaches production.
OutcomePrompts tested across thousands of real assets, with pass and fail scoring that made results comparable across runs and reviewers.
Adopting AI at enterprise scale requires more than a working prototype. A prompt can succeed on a handful of examples and fail under real conditions, and at the scale of millions of assets small inconsistencies compound quickly. Subjective review does not hold up when a decision affects content across the organization. Teams needed a way to test systematically, compare outcomes, and agree on what acceptable output looks like before anything reached production.
The people who needed to make those decisions were not all technical. Legal reviewers, content strategists, and design leads had to be able to evaluate output and set thresholds alongside product and engineering. An evaluation tool that required prompt engineering expertise would have moved the decision back to the engineers and left the partners with the most at stake reviewing after the fact.
The Prompt Playground and Bulk Testing Suite is a shared evaluation environment that runs independently of production while using real assets. Real data at real volume exposes the failure patterns that synthetic test cases do not. Prompts can be run across thousands of assets and the results reviewed for consistency, coverage, and failure patterns, with pass and fail scoring so that outcomes are comparable across runs and across reviewers.
Because everyone reviews the same outputs in the same place, threshold decisions are made cross-functionally before production rather than after a problem has been deployed at scale. Partners without AI expertise contribute directly to evaluation, which shortens the path from question to decision and reduces the number of issues that surface later.
The suite's role is deliberately limited. It informs decisions and does not execute them. When a prompt and configuration meet the agreed criteria, they are promoted into the production enrichment pipeline, which is described in its own case study. Keeping evaluation and execution separate is what allows experimentation to move quickly without putting production at risk.
The suite changed how AI decisions are made on the platform. Evaluation moved from anecdote to evidence, edge cases are found while changes are still inexpensive, and legal and content partners participate in AI decisions rather than reviewing them afterward.