AI Eval in CI

Overview

Test AI agents and LLM outputs the same way you test code — automated evaluations that run in CI, compare against baselines, and fail the build when quality drops. No dashboards to check manually. Just

npx eval run --ci

and a red or green build.

When to Use

• Adding quality gates before deploying AI features to production
• Catching prompt regressions when system prompts or models change
• Comparing model performance (GPT-4o vs Claude Sonnet vs local Llama)
• Validating RAG pipeline accuracy against a test dataset
• Benchmarking agent tool-calling accuracy and latency

Instructions

Strategy 1: Promptfoo (Config-Driven Evals)

Promptfoo is the most popular open-source eval framework. Define test cases in YAML, run against multiple providers, get a comparison matrix.

# promptfooconfig.yaml — Eval configuration
# Tests a customer support agent across 3 models with quality assertions
description: "Customer support agent eval"

providers:
  - id: openai:gpt-4o
  - id: anthropic:messages:claude-sonnet-4-20250514
  - id: ollama:llama3.1:8b

prompts:
  - |
    You are a customer support agent for a SaaS product.
    Respond helpfully and accurately. If you don't know, say so.
    
    Customer message: {{message}}

tests:
  - vars:
      message: "How do I reset my password?"
    assert:
      - type: llm-rubric
        value: "Response explains the password reset process clearly"
      - type: not-contains
        value: "I don't know"
      - type: latency
        threshold: 3000  # Must respond within 3 seconds

  - vars:
      message: "Can I get a refund for my annual plan?"
    assert:
      - type: llm-rubric
        value: "Response acknowledges the refund request and explains the policy"
      - type: not-contains
        value: "I'm an AI"  # Don't break character

  - vars:
      message: "Your product deleted all my data!"
    assert:
      - type: llm-rubric
        value: "Response shows empathy, takes the issue seriously, and offers next steps"
      - type: sentiment
        threshold: 0.3  # Must not be dismissive

  - vars:
      message: "What's the weather in Tokyo?"
    assert:
      - type: llm-rubric
        value: "Response politely redirects to product-related topics"
      - type: not-contains
        value: "Tokyo"  # Should not answer off-topic questions

# Run evals locally
npx promptfoo@latest eval

# Run in CI with threshold — exits non-zero if any test fails
npx promptfoo@latest eval --ci --output results.json

# Compare two prompt versions
npx promptfoo@latest eval --prompts prompt-v1.txt prompt-v2.txt --share

Strategy 2: Custom Eval Framework (TypeScript)

When you need full control — custom scoring logic, database-backed test sets, domain-specific metrics.

// eval.ts — Custom AI eval framework with CI integration
/**
 * Runs evaluation suites against AI agents/LLMs.
 * Each eval defines inputs, expected behavior, and scoring criteria.
 * Exits with code 1 if any score drops below threshold.
 */
import OpenAI from "openai";

interface EvalCase {
  name: string;
  input: string;
  rubric: string;          // What "good" looks like
  threshold: number;       // Minimum score 0-1
  metadata?: Record<string, unknown>;
}

interface EvalResult {
  name: string;
  score: number;
  pass: boolean;
  output: string;
  reasoning: string;
  latencyMs: number;
}

const openai = new OpenAI();

/**
 * Score an AI output using LLM-as-judge.
 * Returns a score 0-1 with reasoning.
 */
async function judge(output: string, rubric: string): Promise<{ score: number; reasoning: string }> {
  const response = await openai.chat.completions.create({
    model: "gpt-4o-mini",  // Cheap model for judging
    messages: [
      {
        role: "system",
        content: `You are an eval judge. Score the AI output against the rubric.
Return JSON: {"score": 0.0-1.0, "reasoning": "brief explanation"}
Score 1.0 = perfect match. Score 0.0 = complete failure.`,
      },
      {
        role: "user",
        content: `Rubric: ${rubric}\n\nAI Output:\n${output}`,
      },
    ],
    response_format: { type: "json_object" },
    temperature: 0,  // Deterministic judging
  });

  return JSON.parse(response.choices[0].message.content!);
}

/**
 * Run a single eval case against your AI agent.
 */
async function runEval(
  agentFn: (input: string) => Promise<string>,
  evalCase: EvalCase
): Promise<EvalResult> {
  const start = Date.now();
  const output = await agentFn(evalCase.input);
  const latencyMs = Date.now() - start;

  const { score, reasoning } = await judge(output, evalCase.rubric);

  return {
    name: evalCase.name,
    score,
    pass: score >= evalCase.threshold,
    output: output.slice(0, 200),
    reasoning,
    latencyMs,
  };
}

/**
 * Run all evals and exit with appropriate code for CI.
 */
async function runSuite(
  agentFn: (input: string) => Promise<string>,
  cases: EvalCase[]
): Promise<void> {
  console.log(`Running ${cases.length} evals...\n`);

  const results: EvalResult[] = [];
  for (const evalCase of cases) {
    const result = await runEval(agentFn, evalCase);
    results.push(result);
    const icon = result.pass ? "✅" : "❌";
    console.log(`${icon} ${result.name}: ${result.score.toFixed(2)} (threshold: ${evalCase.threshold}) [${result.latencyMs}ms]`);
    if (!result.pass) {
      console.log(`   Reasoning: ${result.reasoning}`);
    }
  }

  // Summary
  const passed = results.filter((r) => r.pass).length;
  const failed = results.filter((r) => !r.pass).length;
  const avgScore = results.reduce((s, r) => s + r.score, 0) / results.length;

  console.log(`\n📊 Results: ${passed} passed, ${failed} failed (avg score: ${avgScore.toFixed(2)})`);

  // CI exit code
  if (failed > 0) {
    console.log("\n❌ Eval suite FAILED — quality below threshold");
    process.exit(1);
  } else {
    console.log("\n✅ Eval suite PASSED");
  }
}

export { runSuite, EvalCase };

Strategy 3: GitHub Actions Integration

# .github/workflows/ai-eval.yml
name: AI Eval
on:
  pull_request:
    paths:
      - "prompts/**"
      - "src/agents/**"
      - "eval/**"

jobs:
  eval:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: 20 }
      - run: npm ci

      - name: Run AI evals
        run: npx tsx eval/run.ts --ci
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}

      - name: Post results to PR
        if: always()
        uses: actions/github-script@v7
        with:
          script: |
            const fs = require('fs');
            const results = JSON.parse(fs.readFileSync('eval/results.json'));
            const body = results.map(r => 
              `${r.pass ? '✅' : '❌'} **${r.name}**: ${r.score.toFixed(2)} (${r.latencyMs}ms)`
            ).join('\n');
            github.rest.issues.createComment({
              ...context.repo,
              issue_number: context.issue.number,
              body: `## AI Eval Results\n\n${body}`
            });

Examples

Example 1: Add quality gates to a RAG chatbot

User prompt: "Set up automated evals for our RAG customer support bot. It should test accuracy on 50 known Q&A pairs and fail the deploy if accuracy drops below 85%."

The agent will:

• Create a test dataset from the 50 known Q&A pairs
• Write promptfoo config with llm-rubric assertions for each
• Set pass threshold at 0.85
• Add GitHub Actions workflow that runs on PR to

  prompts/

  or

  src/agents/

• Post eval results as PR comment

Example 2: Compare models before switching

User prompt: "We're considering switching from GPT-4o to Claude Sonnet. Run our eval suite against both and show me which performs better."

The agent will:

• Configure promptfoo with both providers
• Run the existing eval suite against both models
• Generate comparison table with per-test scores, latency, and cost
• Recommend based on score-to-cost ratio

Guidelines

• Eval every prompt change — treat prompts like code; test before deploying
• LLM-as-judge is good enough — GPT-4o-mini costs pennies and correlates well with human judgment
• Use temperature 0 for judges — deterministic scoring reduces noise
• Keep test sets diverse — happy path, edge cases, adversarial inputs, off-topic
• Set realistic thresholds — start at 0.7, tighten as the agent improves
• Track scores over time — log results to detect gradual quality drift
• Separate eval cost from production cost — eval uses cheap judge models, production uses the best
• Cache eval results — don't re-run unchanged tests; hash input+prompt for cache keys
• Run evals on PRs, not just main — catch regressions before merge