Building applications with Large Language Models (LLMs) is transformative, but shipping them can be nerve-wracking. How do you ensure that your latest prompt optimization didn't inadvertently make your customer support agent less helpful? Or that a new model version didn't introduce a subtle bias? Traditional software testing falls short because AI is non-deterministic.
Manual checks are slow, expensive, and don't scale. The answer lies in treating AI quality like any other critical part of your software stack: by automating it.
By integrating rigorous AI evaluation directly into your Continuous Integration/Continuous Deployment (CI/CD) pipeline, you can create an automated quality gate. This ensures that only AI components meeting your performance standards make it to production. This guide will show you how to build that gate using Evals.do.
In traditional development, a unit test verifies a deterministic, binary outcome. 2 + 2 should always equal 4. If it equals 5, the test fails, and the build is blocked.
AI evaluation is different. It measures the qualitative and quantitative performance of a non-deterministic system. You're not just checking for a single right answer; you're scoring things like:
A simple prompt change can cause a regression in any of these areas. Without an automated way to measure them, these regressions can slip past developers and degrade the user experience.
An AI quality gate is an automated step in your CI/CD pipeline that stops a deployment if the AI's performance drops below a predefined threshold. This turns AI quality from a manual afterthought into a mandatory, automated checkpoint.
This is where a dedicated platform like Evals.do becomes essential.
Evals.do is a unified platform to test, measure, and ensure the quality of your AI systems, end-to-end. It's designed to be the engine for your AI quality gate.
With Evals.do, you can:
Let's walk through how you can automate your AI QA process and prevent regressions before they happen.
First, you use the Evals.do SDK to define your evaluation. This "evaluation-as-code" approach means your tests are version-controlled right alongside your application code. You'll specify:
Next, add a new step or job to your CI/CD configuration file (e.g., github-actions.yml, jenkinsfile). This job will be triggered on every pull request or push to your main branch.
# Example for GitHub Actions
jobs:
ai_quality_gate:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v3
- name: Run AI Evaluation on Evals.do
id: run_eval
env:
EVALS_DO_API_KEY: ${{ secrets.EVALS_DO_API_KEY }}
EVALUATION_NAME: "Customer Support Agent Evaluation"
run: |
# Script to trigger the evaluation and check the result
./scripts/run-ai-evaluation.sh
The script in your CI/CD step (run-ai-evaluation.sh) will make an API call to Evals.do. This call initiates the evaluation run you defined in Step 1 against the new version of your code.
This effectively tells Evals.do: "Run our 'Customer Support Agent Evaluation' on this new commit."
After triggering the run, your script will poll the Evals.do API for the final result. The platform provides a clear, concise JSON output once the evaluation is complete.
{
"evaluationRunId": "run_a3b8c1d9e0f7",
"evaluationName": "Customer Support Agent Evaluation",
"status": "Completed",
"overallResult": "FAIL",
"timestamp": "2023-10-27T10:00:00Z",
"summary": {
"totalTests": 150,
"passed": 135,
"failed": 15,
"passRate": 0.9
},
"metricResults": [
{
"name": "accuracy",
"averageScore": 4.1,
"threshold": 4.0,
"result": "PASS"
},
{
"name": "helpfulness",
"averageScore": 4.3,
"threshold": 4.2,
"result": "PASS"
},
{
"name": "tone",
"averageScore": 4.4,
"threshold": 4.5,
"result": "FAIL"
}
]
}
Your CI/CD script simply needs to check the overallResult field. In the example above, the tone metric fell below its threshold of 4.5, causing the overallResult to be "FAIL".
Your script can then use this outcome to pass or fail the CI/CD job.
# Inside run-ai-evaluation.sh
# ... (API call logic to get the JSON result) ...
OVERALL_RESULT=$(echo $JSON_RESULT | jq -r '.overallResult')
if [ "$OVERALL_RESULT" == "FAIL" ]; then
echo "AI Quality Gate FAILED. A metric fell below its threshold."
exit 1 # Fails the CI/CD job
else
echo "AI Quality Gate PASSED."
exit 0 # Allows the pipeline to continue
fi
With this in place, the pull request is automatically blocked. The developer is notified immediately that their change caused a performance regression, complete with detailed metrics on what failed.
Integrating AI quality assurance into your CI/CD pipeline is no longer a "nice-to-have"—it's a core practice for building reliable, high-quality AI products. This automated approach allows you to:
By making AI evaluation a non-negotiable step in your development lifecycle, you can finally move from hoping your changes work to knowing they do.
Ready to automate your AI quality assurance? Visit Evals.do to learn more and build your first AI quality gate.
Q: What is Evals.do?
A: Evals.do is an agentic workflow platform for defining, running, and monitoring evaluations for AI components. It allows you to systematically test everything from individual AI functions to complex, multi-step agent behaviors against predefined datasets and metrics to ensure quality and reliability.
Q: Can Evals.do integrate with my CI/CD pipeline?
A: Yes. Evals.do is designed to be a core part of your MLOps and development lifecycle. You can trigger evaluation runs via API as part of your CI/CD pipeline to automatically gate deployments based on performance thresholds.
Q: What's the difference between an evaluation and a unit test?
A: While a unit test checks for deterministic, binary outcomes (pass/fail), an evaluation measures the qualitative and quantitative performance of non-deterministic AI systems. Evals measure things like helpfulness, accuracy, and adherence to style, which often require more complex scoring.