PromptisePromptise
Why PromptiseUse casesRoadmapDocs
GitHubGet started
← All questions> Cost, latency and reliability

How do I design an evaluation set for prompts?

Assemble 20–100 real cases that represent your distribution—easy, typical, and tricky. Label expected outputs or acceptance criteria. Include edge cases and near-misses. Keep it small enough to run daily. Track metrics that matter (accuracy, adherence, time to fix) and review failures. Refresh the set as your data shifts.

In practice

Tag examples by failure mode (missing data, ambiguity, formatting) to spot patterns quickly.

Where to start

  1. [01]Collect real examples and redact sensitive info.
  2. [02]Write gold outputs or rubrics for each.
  3. [03]Automate a daily run and dashboard.
  4. [04]Rotate in new examples monthly.
prompt evaluationLLM benchmark setprompt testing dataset

Related questions

// 001How do I prevent repetitive outputs across runs?// 002What’s a good fallback strategy when a prompt fails?// 003Which analytics matter for prompts in production?// 004How do I improve internal search with prompt engineering?

Build it properly.

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
Promptise - AI Framework LogoPromptise

The foundation layer for agentic intelligence. Build, secure and operate autonomous AI systems with Promptise Foundry.

pip install promptise

[01] Foundry

  • MCPcast
  • The Promptise Agent
  • Reasoning Engine
  • MCP
  • Agent Runtime
  • Prompt Engineering
  • Execution Engine
  • Agent Identity

[02] Resources

  • Documentation
  • GitHub
  • Guides
  • Learning Paths
  • Questions

[03] Company

  • About
  • Terms of Service
  • Privacy Policy
  • Cookie Policy
  • Subprocessors

© 2026 Promptise by Manser Ventures. All rights reserved.

Open source · Python