Preventing AI Regressions: Why Your CI/CD Pipeline Needs Continuous Evaluation

You've done it. After weeks of prompt engineering, data curation, and testing, your new AI-powered customer support agent is working beautifully. It's empathetic, accurate, and incredibly helpful. You deploy it, and the team celebrates.

A week later, you push a small update—a minor tweak to a system prompt or a switch to a newer, "better" base model version. Suddenly, user complaints trickle in. The agent is giving curt responses, hallucinating facts, and failing at tasks it handled perfectly before. Your celebrated AI has regressed.

This scenario is the silent nightmare for teams building with Large Language Models (LLMs). Unlike traditional software where a bug is often a clear break, AI regressions are a subtle, insidious decay in quality. The solution isn't more manual spot-checking; it's a fundamental shift in how we test AI. It's time for continuous evaluation.

The Sneaky Nature of AI Regressions

In traditional software, a regression is when a change breaks existing functionality. For AI, a regression is a degradation in performance or quality. It’s often not a binary pass/fail but a slide along a spectrum of quality.

These regressions can manifest in countless ways:

• Tonal Shifts: An agent designed to be friendly suddenly becomes formal and robotic.
• Accuracy Drops: A summarization tool starts missing key details or misinterpreting source documents.
• Helpfulness Decline: A workflow that previously guided users to a solution now gets stuck in a loop.
• Increased Hallucinations: An agent starts fabricating information more frequently after a change to its RAG knowledge base.

Why do they happen? The dynamic nature of the AI stack means a change in one layer can have unpredictable effects on the entire system. Common culprits include:

• Updates to the underlying LLM (e.g., gpt-4-turbo-2024-04-09 behaving differently from its predecessor).
• Minor adjustments to system prompts or few-shot examples.
• Changes in the data format or content used for Retrieval-Augmented Generation (RAG).
• Modifications to the tools and functions an AI agent can call.

Manual spot-checking a few queries after each change simply isn't scalable or reliable enough to catch these subtle shifts. You need a systematic, automated, and quantifiable approach.

From Unit Tests to Eval Tests: Embracing Evaluation-Driven Development

In software engineering, we rely on CI/CD (Continuous Integration/Continuous Deployment) pipelines to maintain quality. Every code change automatically triggers a suite of unit, integration, and end-to-end tests. If tests fail, the build is blocked, preventing bugs from reaching production.

We must apply the same rigor to AI development. This is where Continuous Evaluation comes in.

By integrating AI evaluations directly into your CI/CD pipeline, you can treat AI quality as a testable, blockable step in your development lifecycle. This practice, which we call Evaluation-Driven Development, turns AI quality from a subjective art into an engineering discipline.

The workflow looks like this:

1. Commit: A developer pushes a change to the AI system (a new prompt, an updated model, etc.).
2. Trigger: The commit automatically triggers your CI/CD pipeline (e.g., via GitHub Actions).
3. Evaluate: The pipeline makes an API call to a platform like Evals.do, initiating an evaluation run. The updated AI component is tested against a standardized dataset of inputs.
4. Grade & Report: Evals.do runs the tests and grades the AI's output against a set of predefined metrics (like accuracy, helpfulness, or tone). It then returns a structured report with scores and a clear pass or fail status.
5. Decide: If the evaluation report shows a performance drop below your set threshold—a regression—the CI/CD pipeline automatically fails the build. The faulty change is blocked from being merged or deployed, protecting your users from a degraded experience.

Implementing Continuous Evaluation with Evals.do

Conceptually, this makes perfect sense. But in practice, how do you manage the datasets, define the metrics, and run these evaluations at scale? This is the problem Evals.do was built to solve.

Evals.do provides the platform to define, run, and analyze evaluations as a core part of your development process. It empowers you to quantify AI performance with code.

1. Define Evals as Code

Instead of ad-hoc spreadsheets and manual checks, Evals.do allows you to define your entire evaluation suite as code. You version-control your test datasets, metrics, and scoring thresholds right alongside your application code. This ensures your tests are repeatable, transparent, and consistent.

2. Integrate with a Simple API Call

Evals.do is designed for a seamless developer experience. Triggering a comprehensive evaluation run from your CI/CD pipeline is as simple as making an API call. This low-friction integration means you can add robust AI testing without overhauling your existing workflows.

3. Get Quantifiable, Actionable Results

The output of an evaluation isn't a subjective "looks good." It's a precise, structured JSON object that your pipeline can programmatically understand.

{
  "evaluationId": "eval_abc123",
  "target": "customer-support-agent:v1.2",
  "dataset": "customer-support-queries-2024-q3",
  "status": "completed",
  "summary": {
    "overallScore": 4.35,
    "pass": false,
    "metrics": {
      "accuracy": {
        "score": 4.1,
        "pass": true,
        "threshold": 4.0
      },
      "helpfulness": {
        "score": 4.05,
        "pass": false,
        "threshold": 4.2
      },
      "tone": {
        "score": 4.55,
        "pass": true,
        "threshold": 4.5
      }
    }
  },
  "timestamp": "2024-09-12T14:30:00Z"
}

In this example, even though accuracy and tone passed, a dip in the helpfulness score caused the overall evaluation to fail. Your CI/CD pipeline can instantly catch this regression and block the deployment, alerting the developer to investigate the "why" before it impacts users.

Build AI with Confidence

AI regressions are a serious threat to the reliability and trustworthiness of your products. Relying on manual checks is like navigating a minefield blindfolded—it’s only a matter of time before something goes wrong.

By embracing continuous evaluation and integrating it into your CI/CD pipeline, you can move from hoping your AI works to knowing it does. This systematic approach allows you to innovate faster, experiment with new models and prompts, and deploy changes with confidence, secure in the knowledge that a safety net is there to catch any drop in quality.

Ready to stop regressions and ensure the quality of your AI functions, workflows, and agents? Discover how Evals.do can bring Evaluation-Driven Development to your team.