You've built a groundbreaking AI feature. The prompts are crafted, the models are tuned, and your unit tests are all green. You're ready to ship. But a nagging question remains: how do you really know it's good? Will it be helpful? Is the tone right? Will it hallucinate under pressure?
In the age of generative AI, traditional software testing methodologies are hitting their limits. While essential, unit tests that check for predictable, binary outcomes are simply not equipped to measure the quality of non-deterministic systems like Large Language Models (LLMs).
This is where a dedicated AI evaluation framework becomes not just a nice-to-have, but a mission-critical component of your development lifecycle. It's time to go beyond unit tests and embrace a new paradigm for AI quality assurance.
Unit tests are a cornerstone of software engineering. They excel at verifying logic with deterministic outputs. Does 2 + 2 equal 4? Does a function return a null value when it should? These questions have clear, unambiguous pass/fail answers.
AI, particularly LLMs, operates in a world of ambiguity and nuance. Consider a customer support AI agent. You can't write a simple unit test to verify if its response is "good." A "good" response has many dimensions:
Testing for these qualitative attributes requires a system designed to measure performance, not just verify correctness.
An AI evaluation framework provides the tools to systematically test, measure, and ensure the quality of your AI systems, from end-to-end. It allows you to move from guessing to knowing, enabling you to Measure, Monitor, and Improve every AI component you deploy.
At its core, AI evaluation involves running your AI function, workflow, or agent against a predefined dataset and scoring its outputs against key performance metrics. This is precisely what platforms like Evals.do are built for.
With Evals.do, you define evaluations as code using a simple SDK, specifying the target component, the metrics you care about, and the evaluators that will score the results.
Imagine you're evaluating a new version of your customer support agent. An evaluation run might produce a result like this:
{
"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"
}
]
}
This JSON report tells a powerful story that a simple pass/fail unit test never could. Even with a 90% pass rate, the overall evaluation FAILs. Why? The tone of the agent's responses dipped just below the required threshold. This granular insight is invaluable. It allows you to pinpoint the exact dimension of performance that has regressed, fix it, and re-run the evaluation before a single user is impacted.
The true power of AI evaluation is unlocked when it's integrated directly into your development workflow. Evals.do is designed to be a core part of your MLOps stack.
By triggering evaluation runs via an API call within your CI/CD pipeline, you can automatically gate deployments.
This continuous feedback loop transforms your AI quality assurance from a reactive, manual process into a proactive, automated safeguard for your product and your brand. It's how you move from hoping your AI is good to knowing it is.
In a competitive landscape, the quality and reliability of your AI are your biggest differentiators. Building great AI products requires more than just clever prompting; it requires rigorous testing and a commitment to quality.
While unit tests will always have their place, they are not sufficient for the non-deterministic world of AI. To ensure quality, mitigate risk, and consistently improve your user experience, you need a dedicated platform for AI Evaluation and LLM Testing.
By embracing a framework that allows you to rigorously test, evaluate, and monitor the performance of your AI functions, workflows, and agents, you can finally stop guessing and start shipping with confidence.
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 kind of AI components can I evaluate?
A: You can evaluate a wide range of components with Evals.do, including large language model (LLM) responses, individual functions, multi-step workflows, and fully autonomous agents. The platform is designed to be flexible and adaptable to your specific AI architecture.