Why evaluating LLMs is challenging

Large language models are not deterministic programs. They produce probabilistic outputs that can vary for the same prompt, which complicates efforts to measure reproducibility, consistency, and quality. Traditional software testing methods and broad leaderboards do not capture the variability and domain specificity that many production use cases require.

What Stax brings to developers

Stax, released by Google AI, is an experimental developer tool designed to provide a structured approach to assessing and comparing LLMs. The tool moves evaluation away from abstract global scores and toward criteria that matter to each team. Developers can define evaluation workflows that reflect their specific requirements, whether that is factual grounding, safety constraints, or fluency expectations.

Key features

Quick Compare for prompt testing lets you run different prompts across models side by side, making it easier to spot how prompt design or model choice affects outputs. This feature reduces time spent on trial and error when iterating on prompts.

Projects and Datasets enable larger, reproducible evaluations. Teams can assemble structured test sets and apply consistent evaluators across many samples. This helps simulate realistic workloads and track model behavior across a variety of inputs rather than relying on single-example checks.

Custom and pre-built evaluators are at the heart of Stax. Developers can author their own autoraters tailored to niche requirements or use built-in evaluators that target common dimensions:

• Fluency: grammatical correctness and readability
• Groundedness: factual consistency with reference materials
• Safety: avoidance of harmful or undesired content

This flexibility allows teams to align evaluations with domain-specific standards instead of one-size-fits-all metrics.

Analytics and interpretability

Stax provides an Analytics dashboard that surfaces trends and comparisons. Rather than presenting a single-number score, the dashboard helps teams inspect model behavior, compare outputs across evaluators, and identify patterns that matter for production readiness. These insights support informed decisions when selecting or tuning models.

Practical use cases

Stax is useful across several stages of model development and deployment:

• Prompt iteration: refine prompts to achieve more consistent and reliable outputs
• Model selection: compare candidate LLMs before committing to a production choice
• Domain-specific validation: test outputs against organizational or industry rules
• Ongoing monitoring: re-run evaluations as datasets and requirements evolve

Why it matters for production teams

For teams deploying LLMs in real applications, Stax offers a way to replace ad-hoc checks with repeatable, transparent evaluation workflows. By combining quick comparisons, dataset-level testing, customizable autoraters, and analytics, Stax helps teams better understand model strengths and weaknesses under the conditions that matter most to their users.