Navigating the Data: A Guide to Choosing the Right AI Metrics

You’ve built a groundbreaking AI application. The prompts are working, the Large Language Model (LLM) is responding, and your agent is completing tasks. But a critical question remains: how do you know if it's actually good? More importantly, how do you ensure it stays good with every code change, every model update, and every new piece of data?

In the world of non-deterministic AI, traditional software testing isn't enough. A simple pass/fail unit test can't capture the nuance of a helpful response, the subtlety of brand tone, or the accuracy of a complex summary. To ship with confidence, you need to move beyond basic tests and embrace a robust AI evaluation strategy built on the right set of metrics.

This guide will walk you through choosing the right metrics to comprehensively measure your AI's performance, from discrete functions to complex agentic workflows.

Beyond Pass/Fail: Why AI Needs a New Measurement Paradigm

A traditional unit test checks for a deterministic, binary outcome. Does 2 + 2 equal 4? Yes or no. This is perfect for predictable code.

AI systems, especially those powered by LLMs, are probabilistic. For the same input, you might get slightly different outputs. The goal isn't a single "correct" answer but a high-quality one. This is the core difference between a unit test and an AI evaluation.

• Unit Test: Checks if a function works as coded.
• Evaluation: Measures if an AI's output is effective, accurate, and aligned with its goals.

An evaluation measures qualitative aspects like helpfulness, safety, and adherence to style—things that require sophisticated scoring, not just a simple boolean check.

A Framework for Selecting Your AI Metrics

A comprehensive AI Quality Assurance process doesn't rely on a single score. It uses a balanced scorecard of metrics across different categories. Let's break them down.

1. Task Performance & Accuracy Metrics

These metrics measure how well the AI performs its core function. They are often quantitative and can be automated.

• Factual Accuracy: Does the model's output align with a ground truth source? This is crucial for Q&A bots and data extraction tools.
• Semantic Similarity: How close in meaning is the AI's output to a reference answer? This is more flexible than an exact match and excellent for tasks like summarization or paraphrasing.
• Keyword/Regex Matching: A simpler method to check for the presence (or absence) of specific words or patterns. Useful for ensuring key information is included or restricted content is avoided.
• Tool Usage & Function Calling: For more complex agents, you need to measure whether the AI correctly identifies when to use a tool and calls it with the right arguments.

2. Qualitative & Behavioral Metrics

This is where LLM testing becomes truly nuanced. These metrics gauge the character and quality of the interaction.

• Helpfulness & Relevance: Does the response actually address the user's query? Is it on-topic?
• Tone & Style Adherence: Does the AI's voice match your brand? Is it professional, casual, witty, or empathetic as required?
• Conciseness: Does the model answer directly without unnecessary verbosity?
• Safety & Harmfulness: A critical check to ensure the model isn't generating toxic, biased, or inappropriate content.

Imagine an evaluation run for a customer support agent. The agent might be highly accurate, but if its tone is robotic and unhelpful, the user experience suffers. A good evaluation platform highlights this discrepancy.

For example, a report from an AI evaluation platform like Evals.do might look like this:

{
  "evaluationRunId": "run_a3b8c1d9e0f7",
  "evaluationName": "Customer Support Agent Evaluation",
  "status": "Completed",
  "overallResult": "FAIL",
  "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"
    }
  ]
}

In this run, despite high accuracy and helpfulness, a failure to meet the tone threshold caused the overall evaluation to FAIL. This is an actionable insight that prevents a poor user experience from being deployed.

3. Operational & System Metrics

An amazing AI that is too slow or expensive is not a viable product. Operational metrics are essential for workflow monitoring and production readiness.

• Latency: How long does it take to get a response? High latency can kill user engagement.
• Cost: How much does each AI call or workflow execution cost? This is vital for managing budgets and ensuring profitability.
• Throughput: How many requests can the system handle concurrently? This is key for scaling your application.

From Metrics to Actionable Insights with Evals.do

Choosing metrics is the first step. The next is implementing a system to consistently measure, monitor, and improve upon them. This is where a dedicated agentic workflow platform like Evals.do becomes indispensable.

Evals.do provides a unified platform to turn your chosen metrics into an automated, end-to-end evaluation pipeline.

1. Define as Code: Define your evaluations using a simple SDK, specifying the component to test, the metrics to measure, the test dataset, and the evaluators (from automated LLM-as-a-judge scorers to human review).
2. Evaluate Everything: Systematically test a wide range of components, from individual LLM responses and AI functions to multi-step workflows and autonomous agent performance.
3. Integrate and Automate: Trigger evaluation runs via API as part of your CI/CD pipeline. Automatically gate deployments based on performance thresholds, ensuring that only high-quality AI changes make it to production.
4. Measure, Monitor, Improve: Go from abstract metrics to concrete, actionable reports. Track performance over time, identify regressions, and gain the confidence you need to innovate rapidly.

Stop guessing if your AI is effective. Start measuring. By choosing the right metrics and implementing a robust evaluation framework, you can ensure the quality, accuracy, and reliability of your AI systems.

Ready to take control of your AI's performance? Discover how Evals.do can provide the clarity you need to build and ship with confidence.