CI/CD Regression Testing for Prompts

Prompt engineering without continuous integration is reckless. A minor tweak to a system prompt intended to fix one edge case frequently causes regressions across 15 other scenarios.

Setting up an Eval Workflow

Configure a GitHub Actions job that runs against your golden dataset whenever a prompt file or tool schema changes:

name: Agent Evals
on:
  pull_request:
    paths:
      - 'prompts/**'
      - 'tools/**'

jobs:
  run-evals:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run Trajectory Benchmarks
        env:
          OPENAI_API_KEY: $
        run: |
          npm install
          npm run test:evals -- --threshold=0.95

LLM-as-a-Judge Scoring

For open-ended subjective outputs, use a more capable judge model (e.g. GPT-4o or Claude 3.5 Sonnet) with a rubrics-based prompt:

Accuracy: Did the final response accurately address all criteria? (1–5)
Tool Efficiency: Did the agent avoid redundant or superfluous tool calls? (Pass/Fail)
Tone & Safety: Did the model adhere strictly to brand and guardrail guidelines? (Pass/Fail)

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