### What Are Evals? Evals (short for _evaluations_) are structured tests that measure how well an AI model performs on specific tasks. They give a consistent way to compare models, track improvements, and decide which model is best suited for a job. Instead of just asking “does the model seem smart,” evals define measurable standards. Example: - Accuracy on math problems - Precision in medical diagnosis - Relevance of search results - Tone in customer support replies ### Why They Matter - **Model choice**: There are dozens of AI models, each with different strengths. Evals help you pick the one that performs best for _your_ use case. - **Quality control**: Evals act like continuous report cards, spotting when a model drifts, degrades, or produces errors. - **Customization**: Startups can design evals tailored to their domain (e.g., legal, healthcare, finance) and use them to tune models. - **Switching advantage**: With good evals, a company can swap in the newest, strongest model quickly and keep its edge. ### Example Imagine you’re building an AI for financial analysis. Off-the-shelf, one model might be better at reasoning with numbers, another at explaining in plain English. A set of evals—say, “Can it summarize a 10-K filing?” or “Does it calculate ratios correctly?”—will show which model is better for your customers. ### The Bigger Picture Evals are becoming the **moat** for AI companies. Anyone can rent compute or plug into an API, but owning a library of well-designed evals means you uniquely know what “good” looks like in your domain. That knowledge compounds over time.