Evaluating large language models (LLMs) is not straightforward. Unlike traditional software testing, LLMs are probabilistic systems. This means they can generate different responses to identical prompts, which complicates testing for reproducibility and consistency. To address this challenge, Google AI has released Stax, an experimental developer tool that provides a structured way to assess and compare LLMs with custom and pre-built autoraters.
Stax is built for developers who want to understand how a model or a specific prompt performs for their use cases rather than relying solely on broad benchmarks or leaderboards.
Why Standard Evaluation Approaches Fall Short
Leaderboards and general-purpose benchmarks are useful for tracking model progress at a high level, but they don’t reflect domain-specific requirements. A model that does well on open-domain reasoning tasks may not handle specialized use cases such as compliance-oriented summarization, legal text analysis, or enterprise-specific question answering.
Stax addresses this by letting developers define the evaluation process in terms that matter to them. Instead of abstract global scores, developers can measure quality and reliability against their own criteria.
Key Capabilities of Stax
Quick Compare for Prompt Testing
The Quick Compare feature allows developers to test different prompts across models side by side. This makes it easier to see how variations in prompt design or model choice affect outputs, reducing time spent on trial-and-error.
Projects and Datasets for Larger Evaluations
When testing needs to go beyond individual prompts, Projects & Datasets provide a way to run evaluations at scale. Developers can create structured test sets and apply consistent evaluation criteria across many samples. This approach supports reproducibility and makes it easier to evaluate models under more realistic conditions.
Custom and Pre-Built Evaluators
At the center of Stax is the concept of autoraters. Developers can either build custom evaluators tailored to their use cases or use the pre-built evaluators provided. The built-in options cover common evaluation categories such as:
- Fluency – grammatical correctness and readability.
- Groundedness – factual consistency with reference material.
- Safety – ensuring the output avoids harmful or unwanted content.
This flexibility helps align evaluations with real-world requirements rather than one-size-fits-all metrics.
Analytics for Model Behavior Insights
The Analytics dashboard in Stax makes results easier to interpret. Developers can view performance trends, compare outputs across evaluators, and analyze how different models perform on the same dataset. The focus is on providing structured insights into model behavior rather than single-number scores.
Practical Use Cases
- Prompt iteration – refining prompts to achieve more consistent results.
- Model selection – comparing different LLMs before choosing one for production.
- Domain-specific validation – testing outputs against industry or organizational requirements.
- Ongoing monitoring – running evaluations as datasets and requirements evolve.
Summary
Stax provides a systematic way to evaluate generative models with criteria that reflect actual use cases. By combining quick comparisons, dataset-level evaluations, customizable evaluators, and clear analytics, it gives developers tools to move from ad-hoc testing toward structured evaluation.
For teams deploying LLMs in production environments, Stax offers a way to better understand how models behave under specific conditions and to track whether outputs meet the standards required for real applications.
