How to Build AI Benchmarks that Evolve with your Models

In our first post in the Benchmark series, we explored why evaluating large language models (LLMs) is uniquely challenging—and how AI benchmarks offer a solution by bringing structure, repeatability, and objectivity to an otherwise subjective process. Unlike traditional machine learning, where performance can often be measured against a static ground truth, LLMs produce open-ended outputs that require more nuanced methods of evaluation. Benchmarks help fill that gap, offering standardized tasks and scoring frameworks to track model progress over time.

In this article, we’ll break down what makes a benchmark effective: the core components you need, different scoring approaches, and when to use them. We’ll also explore how benchmark strategies should evolve as your system matures—from early prototypes to production-ready applications—so you can evaluate your model in the right way, at the right time.

How do AI Benchmarks Work?

An AI benchmark has two key components: a standardized set of tasks, and a scoring methodology.

First, the standardized set of tasks, much like the held-out test set of traditional machine learning, ensures that metrics you get across runs of the benchmark are comparable to each other. By asking the model to answer the same questions every time, we can begin to get a deeper understanding of where our model is performing well or poorly, and how it has changed over time. This change over time is key – benchmarks are most helpful when we can compare versions of models against each other to look for improvements and regressions. 

Second, the scoring methodology defines how the benchmark evaluates tasks. There are a few different techniques for this, from basic statistical scoring to judgement scoring, which unlocks real understanding of model performance after the prototype stage. Let’s dive a little deeper:

1. Statistical Scoring: these methods evaluate model outputs data using quantitative metrics and tend to be deterministic, scalable, with consistent results across evaluations. There are two general types:
   1. Reference-based metrics: uses a set of rules to score the model’s output against some reference data using deterministic algorithms.
      1. Examples: BLEU scores compare token-level overlap between generated and reference text, so a response like "The cat sat on the mat" vs "A cat was sitting on the mat" might score 0.3 BLEU-2. The MMLU (Massive Multitask Language Understanding) benchmark calculates accuracy against multiple-choice question answers.
      2. Benefits: Deterministic and reproducible results, fast execution, easily verified, very scalable
      3. Limitations: Requires reference data, lacks semantic understanding, can miss nuanced correctness
   2. Code-based scoring: uses programmatic logic to check for specific patterns, formats, or functional requirements. This may include regex patterns, parsing checks, keyword detection, and custom validation functions.
      1. Example: The HumanEval benchmark for code generation uses code-based execution to test whether LLM generated code passes predefined test cases with a pass@k metric (the probability that at least one of the top k-generated code samples for a problem passes the unit tests). Alternatively, an application generating responses in JSON format may be evaluated on validity of output format.
      2. Benefits: Precise requirements, scalable, deterministic, interpretable scoring logic
      3. Limitations: Limited to certain use cases, requires code development, hard to cover edge cases, may be overly restrictive
2. Judgment-Based Scoring: this method uses evaluators (human or AI) to make more qualitative assessments. Statistical metrics fall apart when evaluating LLMs on anything beyond closed-set tasks. BLUE, ROUGE, and others break the moment that your model is generating free text, including advice, explanations, or decisions. This is where judgement based scoring becomes essential. It brings human understanding (or at least the LLM approximation of it) into the loop to evaluate open-ended outputs based on criteria like included or excluded advice, tone, and more – things that can’t be measured by token overlap. There are different ways to implement judgment-based scoring, so here are a few examples to illustrate different sides of the spectrum of sophistication. Depending on your evaluation goals and resource constraints, the most appropriate option may start somewhere in between and adapt as your project evolves.
   1. General quality assessment: a holistic judgment about a model's output based on broad criteria or guidelines (like relevancy, fluency, tone, etc) and applied to all tasks in the test suite.
      1. Example: You write up some instructions to evaluate whether the model’s responses to questions are “relevant” or “irrelevant,” based on your preferences and example Q&A pairs.
      2. Benefits: Very fast to implement, good for early model comparisons
      3. Limitations: Doesn’t reveal why the model failed, reliance on an evaluator’s general knowledge or intuition, more subjective
   2. Rubric evaluation with task-level criteria: each task has an associated rubric created by a domain expert that lays out the criteria for success, each criterion associated with a point value or pass/fail grade. The model’s response is evaluated against the rubric, resulting in a final point score or pass/fail grade per task.
      1. Example: HealthBench from OpenAI uses physician-designed rubrics where each criterion is assigned a point value from -10 to +10. Positive criteria reward desired behaviors like providing essential information or demonstrating empathy, while negative criteria penalize harmful behaviors like misinformation or unsafe advice. The task score is normalized to the maximum possible score.
      2. Benefits: Provides detailed diagnostic info and actionable insights, standardizes evaluation criteria for consistency, and enables targeted improvements
      3. Limitations: Requires domain expertise to create, may be too restrictive or not capture correct aspects of quality
3. Composite Scoring Methods: this approach combines elements of multiple evaluation techniques to balance nuance and consistency. This may look like an ensemble of judges (e.g. the majority response from multiple human and AI judges), a hybrid of statistical and judgment scores (e.g. BLEU score for task overlap combined with judgment score on relevancy), a weighted composite score (e.g. final score weights safety and accuracy at 70% while fluency and helpfulness contribute 30%), or something else.

Each of these scoring techniques can be implemented using different types of evaluators. Human annotators bring domain expertise and nuanced judgment, LLM-as-a-judge offers flexibility and speed, and code-based scoring provides consistency and repeatability. In practice, the best evaluation setups combine nuanced judgement with automation, e.g. rubric guided scoring via LLMs, with expert in the loop spot checks.

Off-the-Shelf or Custom Benchmarks?

Off-the-shelf benchmarks are a practical starting point. They come with predefined tasks and scoring methods, making it easy to plug in your model and get a baseline score. They’re especially useful early on, when you’re validating core capabilities or comparing across models.

But as your system moves closer to production, general-purpose benchmarks often fall short. Here’s why:

• Popular benchmarks are no longer truly “unseen.” Many public benchmarks used for leaderboards have been incorporated into LLM training data. That means your model may have encountered the tasks or scoring logic before, skewing results and undermining the value of the test. We talk more about this in our recent article Everybody is (Unintentionally) Cheating.
• They’re too broad for real-world needs, and don’t handle your edge cases. Off-the-shelf benchmarks can help you assess overall capabilities, but they won’t reflect your specific use case. They won’t capture critical edge cases or areas where your model has historically underperformed.
• They don’t evolve with your system. Once you start fine-tuning or introducing new features, you need benchmarks that reflect the actual complexity and nuance of your application, not just general benchmarks designed for leaderboard comparison. Any edge cases you previously handled need to be reflected, so you don’t experience model regression.

For these reasons, we recommend adapting or building your own benchmark datasets as your model matures. While starting with off-the-shelf benchmarks gives you some insight, a custom benchmark allows you to define what “good” looks like in your own context, track progress meaningfully, and catch regressions before they reach production.

Up next: In the following article, we’ll show you how to run a benchmark dataset using Label Studio.

Evolving Your AI Benchmark Strategy in Five Stages

As your models scale, so too should your benchmarks. In the early phases of your AI project, building out a full benchmark of your own isn't necessarily worth the cost (in both time and money). However, as your models and applications scale from prototype to production, your benchmarks should progress from off-the-self general ones toward custom benchmarks tailored to your specific use cases.

To reflect increasing specificity in both test cases and evaluation criteria as projects grow, this is how a benchmark strategy may evolve:

Stage 1: Proof of Concept

In this stage, you’re looking to prove that the idea you have for a model or system is viable for further research and development

Key Question: Does my model do what I want it to do?

Evaluation Strategy: Easy to compute algorithmic metrics like Precision, Recall, or F1. These metrics, when computed against human annotated test sets, give you a sense of the viability of your model without the overhead of making your system fit another benchmarking schema.

What to Consider:

• Manual creation of 20–50 key test examples to build early intuition
• Focus on confirming basic functionality—not achieving perfection

Stage 2: Deepen Your Understanding

Now that you have a model you think will work, it’s time to put it to the test. 

Key Question: Does my system work and what are the obvious failure patterns?

Evaluation Strategy:  Using off-the-shelf benchmarks allows you to compare your model with generally agreed upon core functionality on a broad set of tasks. While they may not be specific to your use case, these benchmarks are a good starting point to find failure patterns in your model.

What to Consider:

• Use benchmarks like MMLU (subject knowledge), HumanEval (code), or HellaSwag (reasoning)
• Add 20–50 domain-relevant examples and score with basic metrics or manual review
• Start identifying early failure patterns or gaps in alignment

Stage 3: Customization and Domain Specialization, and Analyzing Failure Modes

As your model improves, your benchmark needs to capture the improved functionality. The work you’ve done to improve your model creates a new set of baseline expectations, along with new edge cases to be aware of. It’s crucial at this stage to have a deep understanding of how your model fails systematically, and to provide feedback for improvement.

Key Question: How consistent is performance and what patterns are there in system behavior?

Evaluation Strategy: Creating specialized benchmark tasks is crucial at this stage to capture the nuances of the system you’re building. This can look like domain specific out-of-the-box benchmarks (like HealthBench for healthcare), the adaptation of other existing benchmarks, or creating your own.

What to Consider:

• Use vertical-specific sets like HealthBench or LegalBench when available
• Develop 200–1000 custom examples reflecting user behavior and corner cases
• Layer in rubric-based scoring or LLM-as-a-judge with periodic human review
• Using seen data to find failure points invalidates your benchmark – they must be designed iteratively to ensure that the latest version has been blind tested and that all common errors are properly identified.

Stage 4: Production performance

Ensure production reliability and performance with use case coverage and ongoing monitoring.

Key Question: How does the system perform over time in the real world, and what is its impact?

Evaluation Strategy: Custom Benchmarks are the way to go here. You’ve built out a nuanced, production-ready system, and your benchmark should reflect the key use cases, nuanced responses, and easy-to-miss edge cases that will ensure production viability in the long term.

What to Consider:

• Comprehensive test case coverage across user scenarios and usage patterns (1000+ examples)
• Custom evaluation criteria developed with domain experts and aligned with business requirements.
• Capture of end user interactions and feedback, to integrate into benchmark test cases or scoring criteria
• Ongoing evaluation for performance degradations and drift in usage patterns
• Red teaming benchmarks to incorporate adversarial testing

Stage 5: Continuous evolution

Continuous evaluation in production systems is the key to success. Automate evaluation pipelines and feedback loops to improve and adapt models efficiently.

Key Question: How can our system learn and improve automatically in an environment that’s constantly evolving?”

Evaluation Strategy: Continuously expanding your benchmark to capture new, real-world scenarios gives you confidence in your current and future iterations of the model.

What to Consider:

• Automatically generate new test cases from production patterns
• Run A/B tests on evaluation criteria to validate what predicts performance
• Track model versions and benchmark results for auditability
• Automate benchmark runs to catch issues before they reach users

Coming next: How to run Benchmarks in Label Studio

This is the second post in our AI Benchmarks series - your guide to building effective, scalable AI evaluations. Whether you’re experimenting on your own AI system or you’re a seasoned practitioner looking to strategize an enterprise evaluation workflow, this series has something for you. In the next post, we’ll show you how best to run benchmarks in Label Studio for fast, reliable evaluation with human feedback.

From off-the-shelf assessments to production-ready custom benchmarks, we've helped teams navigate this journey. Reach out to our team when you’re ready to design an evaluation strategy that scales with your LLM development timeline.