Stop Guessing, Start Measuring: How to Build with Confidence in the Age of AI

If you're building with Large Language Models (LLMs), you're probably familiar with "vibe testing"—the frustrating cycle of tweaking a prompt and hoping the output feels better. This art, rather than science, stems from the non-deterministic nature of AI. Unlike traditional software, you can't always rely on a simple unit test to tell you if a change truly improved things.

To move past this guesswork, we need a new approach: rigorous, repeatable evaluation. General benchmarks are useful, but they don't test your specific use case. The key is to create your own custom AI evaluations that align with your unique data and goals. By doing this, you can turn your "secret sauce" into a quantifiable metric that gives you the confidence to ship and iterate faster.

The Evolution of Evaluation: Humans vs. Machines

Historically, human labeling has been the gold standard for evaluating AI outputs, but it's slow and expensive. This is where a more modern approach, using a powerful LLM as a "judge" or "autorater," comes in. An autorater can evaluate another model's output based on a specific set of instructions, providing a scalable and consistent way to check for things like relevance, tone, or factual accuracy.

This is the core idea behind Stax, a new experimental developer tool designed to take the headache out of LLM evaluation. Stax helps you move from "vibe testing" to making data-driven decisions.

How Stax helps you build with confidence:

1. Bring Your Own Data (or Build It)

Stax makes it easy to get started. You can simply upload a CSV of your test cases or use the interface to build a new dataset from scratch, ensuring your tests reflect your real-world use case.

2. Use Out-of-the-Box Autoraters

Don't want to start from zero? Stax provides pre-built autoraters to check for common criteria like coherence, factuality, and conciseness. This allows you to get meaningful results in minutes, not hours.

3. Build Your Custom Autorater

This is where the real power lies. Every company has its own "brand voice" or specific application rules that a generic benchmark can't capture. Stax allows you to define your own unique criteria and build a custom autorater.

• Need a helpful but not overly chatty chatbot? Build a rater for that.

• Need a summarizer that never includes PII? Build a rater for that.

• Need a code generator that matches your team’s style guide? You can build a rater for that, too.

Stop Guessing, Start Evaluating

It's time to treat LLM-powered features like any other part of your production stack—with rigorous testing and robust tooling. Stax helps you understand, iterate, and improve your AI stack with confidence. Stop crossing your fingers and start evaluating.