Flexible LLM Evaluations
Assess your results

Create an evaluation to understand model performance and improve it. Built for the novice and expert alike. Complex LLM evaluations made simple.

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Use-Case Driven Evaluations

Automatic Triggering

Automatically trigger evaluations on each new prompt version, via the API, or ad-hoc on the UI.

Simple Backtests

Connect evaluation pipelines to production history to run historical backtests.

Model Comparison

Compare and contrast different models in a side-by-side view, easily identifying the best performer.

Flexible Evaluation Columns

Choose from over 20 column types, from basic comparisons to LLM assertions and custom webhooks.

Comprehensive Scorecards

Create score cards with multiple metrics to fit your evaluation needs.

Easy yet Powerful

Simple to start, flexible for any use case or team skill level.

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Increase your LLM application performance

Create evaluations to understand how your models are performing. Judge both qualitative and quantitative aspects of performance. Our evaluation system is designed to be flexible for any use case or team skill level.

Maximum Coverage

Whether you want to test for hallucinations or classifcation, our evaluation system can handle it.

Extreme Flexibility

We provide both out of the box evaluations and tools to create your own.

Easy to Understand

Our evaluation system is built to satisy both ML experts and non-techical users.

Seamless Integration

Connect your evaluations to your prompts and datasets to set up an easy CI/CD process. Think Github Actions.

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Frequently asked questions

If you still have questions feel free to contact us at sales@promptlayer.com

How do you design good LLM evaluation datasets?

Good LLM evaluation datasets reflect how the system behaves in production. Teams design them to include diverse, representative inputs with clear ground truth, covering both common “happy path” cases (to ensure core functionality) and known edge cases (to test robustness). Versioning datasets and auditing how they’re created ensures evaluation results remain reproducible, explainable, and trusted as prompts and models change.

When should you use synthetic vs real data for evals?

Synthetic data is most useful early for exploring behavior and covering rare edge cases, while real data, specifically datasets built from production LLM traces, is essential for regression testing and release decisions. Teams usually begin with synthetic datasets, progressively incorporating production-derived data as traffic grows, ensuring evaluations stay representative as prompts and user behavior evolve.

How do teams audit changes to evaluation data?

Teams audit changes to evaluation data by maintaining versioned datasets with full change history. Every update is recorded with ownership and timestamps, and evaluation results are tied to a specific dataset and prompt version. This full traceability ensures that evaluation results are always reproducible and defensible, linking a score to a specific prompt version and a specific version of the test data.

How do we compare cost vs quality tradeoffs across models inside a prompt chain?

Teams compare cost and quality tradeoffs by running identical prompt chains with different models and evaluating results side by side to understand the trade-off between the API cost/latency and the measured quality score or accuracy of the final output.

How do we reduce token usage across long chains?

Token usage increases when long chains carry forward more context than each step actually needs. Reducing token usage in long chains often involves optimizing the context passed between steps. Teams manage this by tightly controlling state: Implementing summarization techniques, using smaller, faster models for intermediate steps, or relying on structured data extraction in one step to minimize the amount of raw text that must be processed by the subsequent prompt.

Flexible LLM EvaluationsAssess your results