How to Automate AI Quality Assurance with Experiments.do and CI/CD

In the fast-paced world of AI development, speed is everything. Teams are constantly iterating on prompts, fine-tuning models, and optimizing RAG pipelines to deliver smarter, faster, and more efficient AI agents. But with this speed comes a critical risk: How do you ensure that your latest "improvement" isn't actually a step backward?

Traditional CI/CD pipelines are brilliant at catching code-breaking changes, but they fall short when it comes to the nuanced, non-deterministic world of AI. A new prompt might work great for one query but fail spectacularly on another. A different LLM might be cheaper but significantly degrade response quality. Shipping these changes without proper validation is a gamble.

This is where integrating AI experimentation directly into your CI/CD workflow becomes a game-changer. By automating the validation of new prompts, models, and RAG configurations, you can ship updates with data-backed confidence and build a powerful safety net against performance regressions.

The New Challenge: Why CI/CD for AI is Different

Deploying AI, especially systems involving LLMs and agentic workflows, isn't like deploying a typical web service. You're dealing with a new class of challenges:

• Non-Deterministic Outputs: The same input might not always produce the exact same output, making simple pass/fail tests unreliable.
• Qualitative Metrics: Performance isn't just about latency and uptime. It's about relevance, accuracy, helpfulness, and tone—metrics that are hard to measure.
• Complex Dependencies: An AI agent's performance is a result of a complex interplay between the LLM, the prompt, the retrieval system (in RAG), and the data. A change in one part can have unforeseen consequences elsewhere.
• Cost Implications: Swapping one model for another (e.g., GPT-4 for a fine-tuned Llama 3) has direct and significant cost implications that must be tracked.

Without a systematic approach, teams are left to manual checks and guesswork, which slows down innovation and introduces the risk of deploying a degraded user experience.

Experiments.do: Your Automated AI Quality Gate

This is precisely the problem Experiments.do was built to solve. It's a platform designed to let you Validate and Optimize AI Agents by running comprehensive experiments on every component of your system.

Think of it as A/B testing for your entire AI stack. You can compare:

• Prompts: Which prompt variation yields more accurate and helpful answers?
• Models: Does claude-3-sonnet provide a better cost-performance balance than gpt-4o-mini for your use case?
• RAG Configurations: Does a new chunking strategy or vector database improve retrieval relevance?

By integrating this capability into your CI/CD pipeline, Experiments.do becomes your automated "AI Quality Gate," ensuring only superior-performing changes make it to production.

How to Integrate AI Experimentation into Your CI/CD Pipeline

The integration is designed to be seamless and API-first. Here’s a conceptual, step-by-step guide to automating your AI quality assurance.

Step 1: Define Your Experiment on Git Push

When a developer pushes a change—like a new prompt for your RAG pipeline—your CI workflow kicks off. The first step is to define an experiment. The current production configuration is your "baseline," and the new change is your "candidate."

You can define this experiment in a simple JSON configuration file that lives in your repository.

{
  "experimentId": "exp-rag-prompt-v3-test",
  "name": "RAG Prompt v3 Performance Test",
  "variants": [
    {
      "id": "prompt-v2_baseline",
      "config": { "prompt_template": "..." }
    },
    {
      "id": "prompt-v3_candidate",
      "config": { "prompt_template": "..." }
    }
  ],
  "evaluation_dataset": "prod-queries-golden-set.csv"
}

Step 2: Trigger the Experiment via API Call

Within your CI/CD tool (like GitHub Actions, GitLab CI, or Jenkins), add a new "AI Validation" stage. This stage makes a secure API call to Experiments.do, submitting your experiment definition.

A workflow step might look like this:

# Example pseudo-code for a GitHub Actions workflow
- name: Trigger AI Validation Experiment
  id: experiment
  run: |
    RESPONSE=$(curl -X POST https://api.experiments.do/v1/experiments \
      -H "Authorization: Bearer ${{ secrets.EXPERIMENTS_DO_API_KEY }}" \
      -d @./config/experiment.json)
    echo "EXPERIMENT_ID=$(echo $RESPONSE | jq -r .experimentId)" >> $GITHUB_ENV

Experiments.do will now run your baseline and candidate variants against a predefined evaluation dataset, automatically collecting the metrics that matter to you—like relevance, latency, and cost.

Step 3: Poll for Results and Analyze the Winner

Your CI pipeline can then periodically poll the Experiments.do API for the status of the experiment. Once complete, it fetches the results.

The JSON response provides a clear, data-driven conclusion:

{
  "experimentId": "exp-rag-prompt-v3-test",
  "name": "RAG Prompt v3 Performance Test",
  "status": "completed",
  "winner": "prompt-v3_candidate",
  "results": [
    {
      "variantId": "prompt-v2_baseline",
      "metrics": {
        "relevance_score": 0.88,
        "latency_ms_avg": 1200,
        "cost_per_query": 0.0025
      }
    },
    {
      "variantId": "prompt-v3_candidate",
      "metrics": {
        "relevance_score": 0.95,
        "latency_ms_avg": 950,
        "cost_per_query": 0.0021
      }
    }
  ]
}

Step 4: Automate the Go/No-Go Decision

This is the Quality Gate. Your pipeline script checks the "winner" field in the results.

• If the "candidate" is the winner: The change is demonstrably better! The pipeline proceeds, automatically promoting the new configuration and deploying it.
• If the "baseline" wins: The change is a performance regression. The pipeline fails, the deployment is blocked, and the team is notified.

This automated feedback loop prevents you from ever unknowingly shipping an AI update that makes your product worse.

Ship AI with Unprecedented Confidence

By integrating Experiments.do into your CI/CD pipeline, you transform your development process. You move from hopeful deployments to data-driven promotions.

The benefits are immediate:

• Prevent Regressions: Automatically catch changes that make your agent dumber, slower, or more expensive.
• Accelerate Innovation: Empower your team to experiment boldly with new prompts and models, knowing a robust safety net is in place.
• Build Trust: Create a reliable, repeatable process for LLM Validation and RAG Evaluation that builds confidence across the team and with stakeholders.
• Optimize Continuously: Make cost, latency, and quality first-class metrics that are tracked with every single change.

Stop guessing and start measuring. Automating your AI experimentation isn't just a best practice—it's becoming essential for any team serious about building and maintaining high-performing AI services.

Ready to build a CI/CD pipeline that guarantees AI quality? Visit Experiments.do to get started and ship your agentic workflows with confidence.