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Prompt management, observability, and evals for AI teams

See what happened. Prove what improved.

Connect observability first to trace production requests and understand quality, cost, and latency. Then use Tables to monitor results and run evaluations, with Prompt Registry keeping approved versions clear for engineers and reviewers.

Connect observability Explore Tables
ExploreObservabilityTablesPrompt RegistryAPI
Quality loop

Trace, evaluate, release

Passing
Evaluation

Compare changes against real examples before they reach users.

Eval score
Latency
Loop

Core surfaces

A simple loop from signal to release.

Move from what happened to what should ship.

01
Observability

Start with the production record.

Capture requests, responses, metadata, cost, latency, and feedback in one timeline.

02
Tables

Turn examples into decisions.

Organize datasets, score experiments, and compare versions against real behavior.

03
Prompt Registry

Ship approved prompt versions.

Manage versions, labels, and release state so engineers and reviewers stay aligned.

04
Workflows

Connect the loop end to end.

Trace multi-step systems and bring evaluation back into the release process.

Reference shortcuts

Go deeper when you need it.

Focused docs for implementation details, release controls, integrations, and updates.

SDKsRelease LabelsWebhooksOpenTelemetrySelf-hostingMCPAPI ReferenceChangelog