Stax, developed under Google Labs, is a specialized toolkit designed to tackle one of the hardest problems in AI: evaluating large language models (LLMs). Instead of relying on "vibe testing"—the subjective, often inconsistent process of just seeing if outputs feel good—Stax lets teams create custom autoraters that measure performance against criteria that actually matter to them. This moves evaluation from gut instinct to structured, data-driven assessment.
The platform integrates seamlessly with your own datasets, making it possible to benchmark models in realistic contexts rather than canned demos. It also supports all major model providers, ensuring flexibility whether your stack runs on GPT, Claude, Gemini, or open-source alternatives. By defining the metrics and dimensions that are most relevant to your use case—like factual accuracy, style adherence, safety, or reasoning—teams can generate reproducible, comparable results across models and iterations.
As part of Google Labs, Stax reflects Google’s broader push to open up experimental AI tools for public use and feedback. Like other Labs projects such as NotebookLM and Workspace AI assistants, Stax gives developers a way to experiment with cutting-edge evaluation techniques before they become mainstream. For companies and researchers scaling LLM-driven products, it provides a much-needed foundation for making confident, evidence-backed choices about which models, prompts, or configurations to deploy.
