Shadow Eval
Shadow eval is an evaluation method in which real production requests are additionally sent to a candidate version. Its outputs are logged and scored but never served, enabling quality comparison under real conditions without exposing users to risk.
A shadow eval runs a new model or prompt version alongside production and scores its outputs without users ever seeing them — zero risk, high insight.
Explanation
Static test sets age fast and only partially reflect real traffic. Shadow evals solve this by using the live request stream: each request runs through both production and candidate, and results are compared via rule checks, model grading or human sampling. This exposes regressions, formatting errors and cost differences before a version goes live. Key requirements are privacy via pseudonymisation, a capped mirroring share for cost control and clear promotion criteria defining how much improvement justifies a switch. Combined with pinned model versions this creates a dependable release process for AI changes.
Marketing Relevance
For teams running AI features in production, shadow eval is the cheapest way to take responsibility for model swaps and prompt changes without using customer interactions as a test bed.
Example
Before switching models a team mirrors ten percent of chatbot requests for two weeks. The analysis shows better answers but 14 percent more schema errors — the switch happens only after the output format is adjusted.
Common Pitfalls
Without a privacy concept, mirroring real requests is risky. Personal content must be pseudonymised and the retention period for shadow logs kept strictly limited.
Origin & History
The pattern comes from classic software deployment, where shadow traffic has secured releases for years. With unstable model versions and the need for continuous quality assurance, it became a standard tool in AI product teams in 2026.
Marketing Use Cases
De-risking model swaps by running the candidate in parallel before cutover.
Evaluating new prompt versions against real rather than synthetic requests.
Cost comparison between candidates under the real request mix.
Early warning for regressions after unannounced provider changes.
Frequently Asked Questions
How does shadow eval differ from A/B testing?
In an A/B test real users see the new variant, which raises both risk and informational value. In a shadow eval the candidate stays invisible, so you measure output quality only, not user behaviour.
What share of traffic should be mirrored?
In practice five to twenty percent of traffic gives reliable signals. The share should track your budget, since every mirrored request incurs additional model cost.
Who scores the shadow outputs?
Usually a combination: automated rule checks for format and mandatory fields, a grader model for content quality, and a small human sample to calibrate the automated scoring.
Related Services
Go deeper: Measurement & attribution · Model comparison 2026