The race to build and deploy generative AI is on. From smarter chatbots to autonomous agents that can execute complex tasks, businesses are rushing to integrate Large Language Models (LLMs) into their products. But with this rush comes a critical, often-overlooked question: How do you know if it's actually working?
Traditional software testing, with its deterministic unit tests and binary pass/fail outcomes, falls short in the world of non-deterministic AI. You can't just assert a == b when the output is a creatively generated paragraph or a complex series of actions.
To ship AI with confidence, you need a new paradigm for quality assurance. This involves breaking down your system into three distinct layers and evaluating each one rigorously. A robust AI evaluation strategy isn't a single test; it's a comprehensive measurement across the entire system.
The most fundamental building block of any AI system is the discrete function. This is a single, self-contained operation.
At this level, evaluation is about precision and control. You are testing the core component in isolation to ensure it behaves as expected. The goal is to establish a baseline of quality for the foundational pieces of your application.
What to measure:
This is the equivalent of a unit test for AI. By systematically testing these functions with a platform like Evals.do, you can compare different prompts, models (e.g., GPT-4 vs. Claude 3), or configurations to find the optimal setup for each specific task.
What's the difference between an AI evaluation and a unit test? 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.
Few real-world AI applications consist of a single function call. More often, you're building multi-step workflows where the output of one function becomes the input for the next.
The performance of a workflow is not merely the sum of its parts. Errors, biases, and latency can compound at each step. A perfectly good summarization function might fail miserably if the document retrieval function preceding it pulls the wrong information.
What to measure:
Evaluating at this layer is critical for understanding the system's practical performance. Evals.do is designed as an agentic workflow platform that allows you to define, run, and monitor these multi-step evaluations, ensuring your interconnected components work in harmony.
The final and most complex layer is the autonomous agent. An agent isn't just a linear chain; it's a system that can perceive its environment, reason, plan, and execute a series of actions—often in loops—to achieve a high-level goal.
Here, you're not just testing a predefined path. You're evaluating the agent's emergent behavior and its ability to navigate an unpredictable environment.
What to measure:
This is the ultimate test of your AI's readiness for the real world. Agent performance monitoring is essential for building trust and ensuring your AI doesn't go off the rails.
Manually checking outputs at these three levels is impossible to scale. You need a systematic, automated approach. This is where a dedicated AI evaluation platform becomes indispensable.
Evals.do provides a unified platform to evaluate AI performance, end-to-end. From discrete functions to complex agentic workflows, you can define evaluations as code, run them against predefined datasets, and measure performance against critical metrics.
An evaluation run report might look something 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"
}
]
}
Notice how even with a 90% pass rate and passing scores on accuracy and helpfulness, the overall run fails because the tone metric missed its threshold. This level of granular insight is crucial for pinpointing weaknesses before they impact users.
By integrating these evaluation runs directly into your CI/CD pipeline, you can automatically gate deployments based on performance. If a new change causes a regression in quality, the build fails—preventing you from shipping a degraded experience.
Don't just build AI. Build AI that you can trust. Stop guessing, and start measuring.
Ready to ensure the quality, accuracy, and reliability of your AI? Visit Evals.do to learn how to ship with confidence.