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Prompt Testing Studio

Build a prompt testing environment where you can iterate on prompts, run test cases, compare outputs side-by-side, and track version history.

Best for: Prompt engineers, developers, and anyone iterating on LLM prompts

What You Get

  • -Prompt version management
  • -Test case library with expected outputs
  • -Side-by-side output comparison
  • -Automated evaluation metrics
  • -Prompt performance history

Step by Step

1. Set up the database

Create PostgreSQL tables: prompts (id, name, description, system_prompt, version, created_at), test_cases (id, prompt_id, input_text, expected_output, pass_criteria), and test_runs (id, prompt_id, test_case_id, output, passed, latency_ms, cost, created_at).

2. Build the prompt editor

Create a code editor component where users write system prompts. Add version management: save a new version with a name and change description. Display version history with timestamps and the diff between versions.

3. Create test cases

Allow users to define test cases: input text, expected output description, and optional pass/fail criteria (string match, contains, regex, JSON schema validation). Store each test case linked to a prompt version.

4. Run comparisons

Implement a comparison runner: select two prompt versions and run both against the same test cases. Display results side by side with: input, output A, output B, expected output, and pass/fail status per version. Highlight differences in outputs.

5. Track history and performance

Build a dashboard showing: success rate per test case across versions, average output length, latency, and cost per run. Allow rollback to any previous version with one click.

Stack

OpenAINext.jsPostgreSQLVercel AI SDK

Build This

Copy this prompt and paste it into Claude Code, OpenCode, Codex, or Cursor to build this recipe.

Build me a prompt testing studio. It should: 1) Provide a prompt editor where users write system prompts and save versions with names and descriptions. 2) Allow users to define test cases: input text, expected output description, and optional pass/fail criteria. 3) Run a prompt against all test cases and display each input, output, and expected output side-by-side. 4) Compare two prompt versions by running both on the same test cases and showing differences. 5) Track version history with timestamps and allow rollback to any previous version. 6) Include a dashboard showing prompt performance: success rate per test case, average output length, and cost per run.

Common Failure Modes

  • !Test case expected outputs are too vague for automated evaluation
  • !Prompt versions proliferate without clear labeling
  • !LLM output variance makes comparison unreliable
  • !Cost tracking gets expensive with large test suites

Implementation Notes

Start with 3 test cases per prompt. Use temperature=0 for consistent comparison. Add automated eval only for tests with objective pass/fail criteria.

Want prompt testing studio running in your business?

4M Labs can deploy prompt testing studio as a production workflow:

  • Connected to your tools and data sources
  • Secured for your team with proper access controls
  • Deployed with monitoring and error handling
  • Documented for handoff and future maintenance
Book an Implementation Sprint