Stop Guessing, Start Measuring: Why Stax is the Toolkit Your LLM Development Needs

If you are building applications powered by Large Language Models (LLMs), you probably know the drill: you tweak a prompt, run a few tests, and when the results “feel” better, you move on. This process, often called vibe testing, can feel more like intuition than engineering, leaving you unsure if things are genuinely improving.

The challenge lies in the nature of LLMs themselves. They are non-deterministic, meaning that the same input will not always yield the same output. That makes traditional unit tests unreliable. And if you have ever tried to set up a proper evaluation pipeline, wrangling datasets, managing API calls, and parsing outputs, you know it is messy and time-consuming. That is where Google’s new Stax comes in.

What is Stax: The Complete Toolkit for AI Evaluation?

Stax is a developer tool designed to simplify and standardize how we evaluate LLMs. Built on Google DeepMind’s evaluation expertise and Google Labs’ innovation DNA, Stax is purpose-built to take the guesswork out of AI evaluation.
👉 Try Stax now

💡 Note: Stax is currently available only to users in the United States.

Its mission is simple yet powerful: give developers the data, structure, and insights to understand what is actually working in their AI so that they can ship LLM-powered apps faster, safer, and smarter. Think of it as your end-to-end evaluation flywheel, from the first prompt experiment to production-ready releases.

Why Stax Matters: Moving Beyond Generic Benchmarks

Traditional benchmarks tell you how a model performs in general. But your app is not “general.” It is trained on unique data, use cases, and business logic. That is why generic metrics fall short.

Stax lets you define and measure success your way, by evaluating your specific AI stack against your real-world data.

Every Stax Project includes:

• Project Benchmark: Defines what you are testing and how success is measured.
• Dataset: The user prompts used for testing.
• Generated Outputs: The responses from the model(s) under test.
• Evaluations: Human or AI-based scoring.
• Project Metrics: Aggregated results like performance scores and latency.

You can choose between:

1. Single Model Projects: For benchmarking one model or prompt iteration.
2. Side-by-Side Projects: For A/B testing two models or prompts head-to-head.

Datasets: The Foundation of Rigorous Testing

In Stax, datasets are the heart of reliable evaluation. The Datasets Page acts as your central library, managing reusable test sets for consistent measurement.

A strong dataset should:

• Target the exact behavior you want to measure (e.g., refusal of unsafe questions).
• Include diverse cases, common, edge, and adversarial examples.
• Mirror real-world usage (ideally, anonymized production data).
• Focus on quality, not just quantity; hundreds of meaningful data points beat thousands of random ones.

You can upload datasets via CSV (Simple or Chat Format), reuse data from previous projects, or manually build prompts in the playground. Metadata mapping enables dynamic variables like {{metadata.key}}, letting you create context-aware test scenarios.

Evaluators: Automated and Custom Scoring at Scale

Once your datasets are ready, Evaluators do the scoring. Stax supports three major evaluation modes:

1. Manual Human Evaluations: The gold standard for accuracy, though slower and costlier.

2. Heuristic or Code-Based Evaluations: Rule-based checks for clear, objective conditions.

3. LLM-as-Judge (Autoraters): Uses an AI model to rate outputs using a rubric, scalable, fast, and increasingly reliable.

The real power of Stax lies in Custom Evaluators. While it includes ready-made evaluators for Fluency, Safety, and Instruction Following, you can design your own based on brand tone, compliance rules, or business-specific logic.

To build a custom evaluator, you define:

• The Base LLM (your chosen “judge” model).
• The Evaluator Prompt (including rubric and output format).
• Variables (like {{input}}, {{output}}, and metadata).
• Score Mapping (how rubric grades translate to 0.0–1.0).

For accuracy, calibrate your custom evaluators by aligning them against a sample of trusted human ratings. Once tuned, these evaluators become your scalable, automated QA system.

🌟 To learn more about the custom AI evaluator, watch the video below:

Getting Started: Technical Setup and Model Management

To get started, simply connect your API keys, Stax integrates seamlessly with major model providers including Google Gemini, OpenAI, Anthropic Claude, Mistral, Grok, DeepSeek, or custom endpoints.

Using the Model Manager, you can configure model parameters like temperature, max tokens, and seed, or manage your own fine-tuned models and agents.

Tagging features help you organize and track datasets and test cases, making it easy to manage experiments at scale.

From Vibes to Validation

The era of guessing is over. With Stax, you can move from gut-feel testing to measurable, repeatable, data-driven evaluation. Whether you are improving a customer support agent, refining a recommendation system, or validating a safety layer, Stax gives you the evidence you need to move forward confidently.

It is not just another tool; it is a new standard for how we measure progress in AI.

🎥 Prefer watching instead of reading? You can watch the NotebookLM podcast video with slides and visuals based on this blog here.

From Guesswork to Ground Truth

LLM innovation moves fast, but without the right evaluation tools, progress often feels like trial and error. Stax changes that. It turns intuition into evidence, letting you validate ideas, measure improvements, and deploy AI features with confidence. Whether you are fine-tuning prompts, comparing models, or validating your AI stack at scale, Stax gives you the structure and data you need to move forward with clarity. Stop guessing. Start measuring.

Contact us today to learn more about the Google AI ecosystem and how you can leverage the latest tools and experiments like Stax to accelerate your journey in building smarter, more trustworthy AI solutions.