From Manual Spot-Checking to Automated Grading: A Better Way to Test AI

You've just built a new AI-powered feature. Maybe it's a customer support agent, a content summarizer, or a complex data extraction workflow. You feed it a few test prompts. The first output looks great. The second is okay, but a little verbose. The third one hallucinates a fact. You tweak the prompt, re-run the tests, and they look a bit better. "Looks good to me," you say, and push to staging.

If this process feels familiar, you're not alone. But this manual, ad-hoc approach—"spot-checking"—is slow, biased, and dangerously unscalable. In the world of traditional software, we'd never accept such a flimsy testing process. Why should AI be any different?

It's time to move from subjective guesswork to engineered quality. The solution is automated, model-based grading—a method that provides fast, consistent, and scalable feedback on your AI's performance.

The Hidden Costs of Manual AI Testing

Spot-checking might seem sufficient for a prototype, but it quickly becomes a major bottleneck and a source of significant risk as you move toward production.

• It's Unscalable: Can you manually review 1,000 different outputs every time a developer changes a prompt? What about 10,000? As your application grows, manual testing becomes physically impossible, leaving huge blind spots in your quality assurance.
• It's Inconsistent and Biased: What one person considers a "helpful" response, another might find "too casual." Manual feedback is subjective and varies from person to person and even day to day. This inconsistency makes it impossible to reliably track quality over time.
• The Feedback Loop is Excruciatingly Slow: A proper manual review can take hours or even days. By the time the feedback gets back to the developer, they've already moved on to the next task. This slow cycle kills momentum and makes iterative improvement a chore.
• You Can't Catch Regressions: Your "fix" for one edge case might have broken three other, more common scenarios. Without a comprehensive, repeatable test suite, you're flying blind. You have no way of knowing if your AI's quality is actually improving or silently degrading with each change.

A New Paradigm: Quantify AI Performance with Code

To build reliable AI systems, we need to adopt the same rigor we apply to traditional software engineering. This means treating evaluations not as an afterthought, but as a core part of the development cycle. Welcome to automated, model-based grading.

The concept is simple but powerful: use a strong Large Language Model (LLM) as an impartial "grader" to evaluate the output of your target AI function, workflow, or agent.

Here's how it works:

1. Define Quality as Code: You codify your evaluation criteria. Instead of a vague feeling of "good," you define precise metrics like accuracy, helpfulness, tone, or factuality.
2. Create a Golden Dataset: You assemble a standardized set of inputs (test cases) that represent the challenges you expect your AI to handle.
3. Automate Execution: You run your AI component against the entire dataset to generate outputs.
4. Grade at Scale: The grader model scores each output against your predefined metrics, returning structured, quantitative data.

Suddenly, you have a repeatable, objective, and scalable process. You can run thousands of tests in minutes, not days, and get back a consistent report that pinpoints exactly where your system is succeeding and where it's failing.

Evals.do: Your Platform for Evaluation-Driven Development

This is precisely the problem Evals.do was built to solve. We provide an agentic workflow platform to define, run, and analyze evaluations for all your AI components. We help you move from manual spot-checking to a professional, code-driven quality process.

With Evals.do, you can quantify AI performance with code. An evaluation report is no longer a collection of subjective notes; it's a structured object you can act on, just like a unit test result.

Consider this completed evaluation report from Evals.do:

This JSON output tells you everything you need to know at a glance. You can see that customer-support-agent:v1.2 passed its evaluation against the Q3 dataset, meeting the thresholds for accuracy and helpfulness. This isn't guesswork; it's data.

By integrating Evals.do into your CI/CD pipeline, you can enable Evaluation-Driven Development. Trigger a comprehensive evaluation with every pull request, automatically blocking merges that cause a quality regression. This is how you ensure that every change is an improvement and build unshakable confidence in your AI before it ever reaches a user.

Frequently Asked Questions

What is Evals.do?
Evals.do is an Agentic Workflow Platform for defining, running, and analyzing evaluations on your AI components. It allows you to treat evaluation criteria as code, ensuring consistent, repeatable, and scalable testing of your AI functions, workflows, and agents.

Why is evaluating AI components important?
Evaluating AI components is crucial for ensuring they are reliable, accurate, safe, and helpful. Systematic evaluation helps identify weaknesses, prevent regressions, and build trust in your AI-powered services before they impact users.

What kind of metrics can I use with Evals.do?
You can define a wide range of metrics, from objective measures like accuracy and factuality to subjective ones like helpfulness, tone, and user satisfaction. Evals.do supports both automated grading using AI models and human-in-the-loop review processes.

Stop Spot-Checking, Start Engineering

The era of building AI on vibes and manual checks is over. To deliver professional, enterprise-grade AI solutions, you need a professional, enterprise-grade evaluation process. Automated grading provides the speed, scale, and consistency required to innovate confidently.

By treating AI quality as an engineering discipline, you can de-risk your deployments, accelerate your development cycles, and ensure your functions, workflows, and agents meet the highest standards of quality and reliability.

Ready to gain confidence in your AI? Visit Evals.do to learn how to automate your AI evaluation workflow today.