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PromptLayer

Version prompts, test AI outputs and trace production requests together

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What is PromptLayer?

PromptLayer helps engineering teams and domain specialists manage the prompts behind AI applications in a shared visual workspace. Instead of keeping prompt text only in application code, teams can store templates in a registry, compare versions and use release labels to choose which version an application retrieves. Domain experts can edit and test prompts without making an application deployment themselves. Dataset-backed evaluations let a team compare models, replay historical examples and check a new prompt before releasing it. Production request logs connect outcomes to their prompt versions, with traces and cost, latency and token metrics for investigating failures. This makes it useful for teams maintaining AI support agents, structured-generation workflows or other applications where a prompt change needs a repeatable review process. It is infrastructure for building and operating AI features rather than a ready-made general-purpose chatbot; usage allowances, evaluation limits and transaction overages differ by plan.

A shared prompt registry, evaluation workspace and tracing platform for teams building AI applications, with visual version control and usage-based plan limits.

Capabilities and use cases

Dataset-backed evaluations

Run prompts against representative datasets and historical examples, compare models and review scorecards. Regression tests and automated or human grading help teams inspect changes before release.

Prompt registry and release labels

Keep prompt templates outside application code with version history and release labels. Engineers and domain specialists can compare changes and promote or roll back versions through the visual workflow.

Request tracing and usage analytics

Inspect production requests and spans alongside their prompt versions and metadata. Track token use, cost and latency to investigate slow or unsuccessful AI workflows.

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