Measuring What Matters: A Deep Dive into AI Function Metrics

The rise of Large Language Models (LLMs) has unlocked a new frontier of software development. We're building AI-powered functions, workflows, and agents that can summarize documents, answer customer questions, and automate complex tasks. It feels like magic. But with great power comes great unpredictability. How do you really know if your AI customer support agent is helpful and on-brand? How can you be sure a new prompt won't cause your summarization function to hallucinate?

The "it works on my machine" approach of manual spot-checking is no longer enough. To build enterprise-grade AI applications, we need to move beyond hoping for the best and start measuring what matters. We need a systematic way to evaluate AI performance. The key lies in defining and tracking the right metrics.

This is where a new paradigm of AI testing comes in. Platforms like Evals.do are designed to bring the rigor of traditional software engineering to the fuzzy world of AI by allowing you to quantify AI performance with code.

Why Traditional Software Metrics Don't Cut It

In classic software testing, we live in a world of determinism. A function add(2, 2) should always return 4. A unit test can assert this with absolute certainty.

AI functions are different. They are probabilistic. If you ask an AI to "summarize a customer complaint," there isn't one single correct answer. There are thousands of potentially valid, well-written summaries. A simple assert response == expected_output will almost always fail.

This fundamental difference means we need to shift our thinking from "Is the output exactly correct?" to "How good is the output according to a set of principles?" This requires a new toolkit of metrics designed for the nuances of language and reasoning.

The Spectrum of AI Evaluation Metrics

To effectively measure the quality of an AI function, workflow, or agent, you need a balanced scorecard of metrics. These can be broken down into two main categories: objective and subjective.

Objective Metrics: The Verifiable Truth

These are metrics that can often be graded programmatically against a ground truth or a set of clear rules.

• Factuality & Accuracy: Is the information provided by the AI correct? For a question-answering agent, this is paramount. You can check its output against a reference document or known facts.
• Format Adherence: Did the AI return the output in the requested format? If you ask for a JSON object, you need to know if you got a valid JSON object or just a messy string. This can be checked with a simple parser.
• Inclusion of Key Information: For a summarization task, did the AI include all the critical points from the source text? You can check for the presence of specific keywords or named entities.

Subjective Metrics: The Measure of Quality

This is where AI evaluation gets truly powerful—and challenging. These metrics assess the qualitative aspects of the AI's output and are often what separates a functional AI from a delightful one.

• Helpfulness: Does the response actually solve the user's underlying problem? A technically accurate answer might be useless if it's too complex or doesn't address the user's core intent.
• Tone & Brand Voice: Does the AI's language align with your brand's identity? A customer support agent for a bank should sound professional and empathetic, while one for a gaming company might be more casual and witty.
• Conciseness: Is the response clear and to the point? Or does it ramble and include a lot of fluff?
• Safety & Harmlessness: A critical guardrail metric. This ensures the output is free of toxic, biased, unsafe, or otherwise inappropriate content.

From Abstract Metrics to Concrete Scores

Defining metrics is the first step. The second, and more crucial, step is to reliably score them. This is where an evaluation platform becomes essential. Here’s how you turn a concept like "helpfulness" into a hard number that can pass or fail a build.

Grading Your Evaluations

1. Model-based Grading: The most scalable approach. You use a powerful "judge" LLM (like GPT-4o) and provide it with a detailed rubric to score the output of your "target" AI. For example: "On a scale of 1-5, how helpful was this response? A 5 means it completely solved the user's problem."
2. Heuristic Grading: For objective metrics like format adherence, a simple piece of code (e.g., a JSON validator) can return a pass/fail score.
3. Human-in-the-Loop (HITL): For the most nuanced or high-stakes evaluations, human review is the gold standard. A robust evaluation framework should allow you to route specific cases to human graders and integrate their feedback.

Turning Scores into Action

Once you have scores for each metric, you can aggregate them to get a clear, quantitative picture of your AI's performance. With a platform like Evals.do, you can define these evaluations as code, making them repeatable and scalable.

Consider this example evaluation result for a customer support agent:

{
  "evaluationId": "eval_abc123",
  "target": "customer-support-agent:v1.2",
  "dataset": "customer-support-queries-2024-q3",
  "status": "completed",
  "summary": {
    "overallScore": 4.35,
    "pass": true,
    "metrics": {
      "accuracy": {
        "score": 4.1,
        "pass": true,
        "threshold": 4.0
      },
      "helpfulness": {
        "score": 4.4,
        "pass": true,
        "threshold": 4.2
      },
      "tone": {
        "score": 4.55,
        "pass": true,
        "threshold": 4.5
      }
    }
  },
  "timestamp": "2024-09-12T14:30:00Z"
}

Suddenly, the vague question "Is our agent good?" has a concrete answer. We can see the agent has an overall score of 4.35 and passed all its metric thresholds. If a developer later tweaks a prompt and the helpfulness score drops to 3.9, the evaluation will fail, preventing a regression from reaching production.

Making Evaluation a Core Part of Your Workflow

The ultimate goal is to move towards Evaluation-Driven Development (EDD). Just as TDD revolutionized software quality, EDD is doing the same for AI.

By defining your evaluation sets and metrics as code, you can integrate them directly into your CI/CD pipeline. Every time you change a prompt, update a model, or modify an agentic workflow, an automated evaluation is triggered.

This workflow looks like this:

1. Define: Create a dataset of test cases and define your metric-based pass/fail criteria as code.
2. Iterate: Develop your AI function, running evaluations continuously until it meets the quality bar.
3. Commit: Push your code. The CI/CD pipeline automatically runs the full evaluation suite.
4. Deploy: If the evaluation passes, deploy with confidence. If it fails, the build is stopped, and you have a detailed report telling you exactly where the AI fell short.

Ship AI with Confidence

Building with AI doesn't have to be a guessing game. By focusing on measuring what matters—using a balanced scorecard of objective and subjective metrics—you can transform AI quality from an art into a science. This rigorous, code-based approach to LLM testing allows you to catch regressions, compare models, and consistently improve the performance and reliability of your AI agents.

Ready to stop guessing and start quantifying your AI's performance? Get started with Evals.do and ensure your AI functions, workflows, and agents meet the highest standards of quality.