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Code Quality
Iteratively improve code generation quality.
Automated scoring, human feedback loops, and iteration tracking.
Automated Evals
Score generated code at scale with LLM-as-judge and static analysis. Run evals on every generation or on a sample.
Human Feedback
Capture developer ratings to calibrate and improve your evaluation rubrics. Turn signal into better prompts.
Iteration Tracking
Measure quality changes across prompt versions and model updates. Know definitively whether your changes worked.
Automated Evaluation
Score Code Quality Automatically
Set up automated evaluations that score generated code on correctness, style, and adherence to your standards. Run evals on every generation or on a representative sample.
- LLM-as-judge scoring
- Static analysis integration
- Test pass rate tracking
- Custom scoring rubrics
Human Feedback
Calibrate Quality with Developer Judgment
Capture thumbs up/down feedback from developers using generated code. Use that signal to improve your prompts, fine-tune models, and build better evaluation rubrics.
- In-dashboard feedback UI
- Feedback attribution to prompts
- Export datasets for fine-tuning
- Agreement scoring
Version Tracking
Measure Improvement Across Versions
Compare quality scores across prompt versions, model updates, and context changes. Know definitively whether your changes improved generation quality or introduced regressions.
- Version comparison
- A/B testing
- Quality trends over time
- Regression detection
Frequently asked questions
Quality scoring can measure correctness (does the code work?), style (does it follow conventions?), completeness (does it handle edge cases?), and security (does it avoid common vulnerabilities?). You configure which dimensions matter for your use case.
We use a separate language model to evaluate the output of the coding model. You provide a rubric or set of criteria, and the judge model scores each generation against those criteria. This scales to thousands of evaluations without manual review.
Yes. You can pipe generated code through your existing test suite via our evaluation API. Pass/fail results are recorded and tracked against each prompt version.
You can embed a simple thumbs up/thumbs down widget in your internal tools using our feedback API. Feedback is automatically associated with the generation that produced it.
If quality scores drop after a model or prompt change, you will receive an alert. You can roll back to a previous configuration from the dashboard.
Start improving code quality today
Set up your first evaluation pipeline in minutes. Automated quality scoring included on all plans.