Quality evaluation
Turn AI quality from a feeling into a release signal.
Score datasets and production traces with built-in metrics, LLM judges, deterministic code, and human review, then catch regressions before they ship.
One-time starter credit per account on Everstack Cloud. Expires 30 days after signup.
- 01LLM-as-judge
- 02Code scorers
- 03Datasets and baselines
- 04Human annotations
01Operating model
One path from application intent to controlled execution.
Score datasets and production traces with built-in metrics, LLM judges, deterministic code, and human review, then catch regressions before they ship.
01
Define what good means
Combine built-in metrics with your own numeric, boolean, categorical, model-judged, or code-based scoring criteria.
02
Evaluate repeatably
Run the same dataset across prompt, model, or application changes and compare results against an accepted baseline.
03
Connect quality to operations
Link each score to its source trace, schedule recurring runs, and bring people into ambiguous or high-stakes review queues.
02Capabilities
The controls needed to run it in production.
Everstack keeps evaluations connected to the same identity, policy, deployment, and telemetry boundaries as the rest of the platform.
Built-in metrics
Measure answer relevancy, faithfulness, hallucination, bias, and toxicity with ready-to-use scoring rubrics.
LLM judges
Grade outputs against your criteria, source context, and reference answers with a separate evaluation model.
Sandboxed code scorers
Run Python, JavaScript, or TypeScript checks for deterministic and domain-specific evaluation.
Datasets and baselines
Manage representative test cases and promote an accepted run as the comparison point for future changes.
Scheduled and CI runs
Run suites on a schedule or in delivery pipelines so quality regressions surface before rollout.
Human annotation
Route uncertain, sensitive, or sampled cases to reviewers and keep their decisions with the evaluation record.
03Where it fits
Built for real workloads, not isolated demos.
Start with the narrowest useful surface, then connect more of the platform as the workload needs stronger execution, policy, or evidence.
Prompt iteration
Compare versions on the same cases instead of relying on selected examples.
Model migration
Measure the quality and safety impact before changing production models.
Production quality monitoring
Sample live traces and detect changes that latency and error metrics cannot see.
04Questions
What teams usually ask first.
A direct view of how this surface behaves, where it runs, and how it connects to the rest of Everstack.
What is an LLM judge?
It is a separate model that grades an output against a defined rubric, the original input, and optional context or reference answers.
Can evaluations be deterministic?
Yes. Code scorers run in isolated sandboxes and can enforce exact domain rules alongside model-based metrics.
Can an evaluation link back to a trace?
Yes. Evaluation cases can reference the gateway or agent trace that produced the output, which keeps quality evidence connected to runtime behavior.
Reference
The pages you open once you start building.
Continue through the stack