The Hidden Science Behind AI: Understanding Evals

What if you could peek behind the curtain of every major AI breakthrough?

Picture this: OpenAI releases GPT-5, claiming it's "significantly better at mathematical reasoning." Anthropic announces Claude can now "solve complex physics problems with PhD-level accuracy." Google says Gemini has "breakthrough capabilities in chemistry." But what does "better" actually mean? How do they know?

Here's the secret: behind every AI improvement is an army of experts: physicists, mathematicians, chemists working on sophisticated measurement systems called evaluations, or "evals."

That's what you're doing when you work on Outlier projects. You're not just completing tasks. You're building the measurement infrastructure that determines AI success.

Why evals are the hidden foundation of AI

Imagine you're testing a new AI chemistry assistant. During development, it seems to work perfectly, correctly predicting reaction outcomes, suggesting safe procedures, identifying molecular structures. Everything looks great until you deploy it in a real lab, and suddenly it's recommending reactions that could literally explode.

This is exactly why every major AI company obsesses over evaluations. Without rigorous testing, AI models can appear brilliant while being fundamentally broken in ways that only domain experts can detect.

Evaluating AI systems isn't like traditional software testing where you check if code runs without errors. It's more like administering a comprehensive exam to a graduate student: Can it reason through complex problems? Does it recognize when it doesn't know something? Can it explain its thinking process?

What makes a good evaluation

Building effective AI evaluations requires three critical components:

Curated test cases: Domain experts design problems that span from basic competency to edge cases that reveal systematic failures. For mathematics, this might include multi-step proofs, theorem applications, and recognizing when problems have no solution. For chemistry, evaluations test reaction prediction, safety considerations, and molecular behavior under different conditions.

Expert-designed rubrics: Unlike simple right/wrong answers, AI evaluations assess reasoning quality. Can the AI show its work? Does it use appropriate methods? Are there dangerous gaps in its logic? This is where your expertise becomes invaluable—you know what good reasoning looks like in your field.

Human feedback loops: The most sophisticated evaluation systems incorporate ongoing human judgment. When you rate an AI response as "good" or "problematic," you're providing data that shapes how future models will behave.

The three types of evaluations

1. Human evaluations: Direct feedback from domain experts like yourself. This might involve rating AI responses, identifying errors, or providing detailed critiques. While expensive and time-intensive, human evaluations capture nuances that automated systems miss.

2. Code-based evaluations: Automated checks that can run quickly and cheaply. These work well for tasks with clear right/wrong answers like whether generated code runs without errors or if a mathematical calculation is correct.

3. LLM-as-judge evaluations: Using AI systems to evaluate other AI systems. A separate model acts as a "judge," rating responses based on specific criteria. This approach scales better than human evaluation while capturing more nuance than simple code checks.

Your role in shaping AI's future

When you evaluate AI responses on Outlier projects, you're participating in the same evaluation infrastructure used by major AI companies. Your domain expertise helps establish what "good performance" actually means in your field.

Consider what happens when you identify a flawed mathematical proof or flag a dangerous chemical reaction pathway. That feedback doesn't just improve one response, it influences how thousands of future AI systems will approach similar problems.

So next time you're working through those tasks, whether checking if an AI properly balanced a chemical equation or evaluating its approach to a differential equation. Remember that these are exciting times. Maybe someday you'll tell people: "See that AI breakthrough in quantum computing? I helped teach it how to get there.