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← All questions> Cost, latency and reliability

Which analytics matter for prompts in production?

Track cost, latency, attempts per success, validation failures, and business KPI movement. Add prompt version and model ID to logs. Correlate spikes with changes. For support or content, include quality scores and user feedback. Analytics turn anecdote into action.

In practice

Set budgets and alerts per prompt family to catch regressions early.

Where to start

  1. [01]Instrument logs with version and model metadata.
  2. [02]Build a dashboard for core metrics.
  3. [03]Alert on validation failures and cost spikes.
  4. [04]Review metrics with owners weekly.
prompt analyticsmeasure LLM performanceprompt telemetry

Related questions

// 001How do I prevent repetitive outputs across runs?// 002What’s a good fallback strategy when a prompt fails?// 003How do I improve internal search with prompt engineering?// 004How do you optimize prompt performance and reduce costs?

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Promptise Foundry treats prompts as software — typed blocks, versioning, guards and an inspector that shows you every assembly decision.

Prompt engineering in FoundryRead the guides
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Open source · Python