AI EvalOps

Inspiration

As AI becomes increasingly integrated into software applications, one challenge stood out to me: how do developers know when an AI application gets worse after a code change, prompt update, or model change?

Traditional software testing can verify whether code behaves as expected, but AI outputs are less predictable. An application might still run successfully while its answers become less accurate, relevant, or safe.

I wanted to build a tool that brings the reliability and regression-testing practices of traditional software engineering into AI development.

What It Does

AI EvalOps is an automated regression-testing platform for AI applications. Developers can connect an AI endpoint, run predefined test questions, evaluate responses, and compare results against an approved baseline.

The platform tracks quality, performance, and cost metrics to identify regressions. It also integrates with GitHub Actions, allowing teams to automatically fail their CI/CD pipeline when AI quality drops below an acceptable threshold.

To demonstrate this, I built a demo application with four releases. The fourth release intentionally introduces degraded responses and prompt-injection issues, allowing users to see how the platform detects failures and identifies specific regressions through its dashboard.

How I Built It

I built the platform using Python, FastAPI, React, and TypeScript.

I developed the backend to handle test execution, response evaluation, scoring, and baseline comparisons. I built the frontend dashboard to display evaluation results, track metrics, and identify failed test cases.

I also implemented GitHub Actions integration so evaluations can run automatically as part of a development workflow. Approved baselines are stored in the repository, making evaluation results consistent and reproducible across runs.

Challenges I Faced

One of my biggest challenges was designing a reliable way to evaluate AI responses. Unlike traditional tests with predictable outputs, AI responses can vary in wording while still conveying the same meaning. I had to account for this when designing the evaluation and scoring process.

Security was another major challenge. During testing, I identified a DNS-rebinding vulnerability that could expose internal secrets and a large-response memory exhaustion issue. I addressed both problems and added tests to prevent regressions.

I also worked on making the platform easy to deploy as a single service while maintaining appropriate security boundaries between the frontend and API.

What I Learned

Building AI EvalOps strengthened my understanding of AI evaluation, automated testing, CI/CD integration, application security, and production deployment.

Most importantly, I learned that an AI application shouldn't be considered reliable simply because it runs successfully. Its output quality needs to be continuously tested and monitored as the application evolves.

Built With

• actions
• ai
• api
• fastapi
• github
• llm
• python
• react
• typescript