Building a successful LLM product depends on knowing how well it performs. Unfortunately, there are no shortcuts here - relying on a single score from the latest LLM benchmark won’t magically improve your application. It's increasingly clear the path to a successful AI product is built on carefully curated evaluations, rapid iterations, and a clear understanding of the limitations of different evaluation methods and when to use them.
Before discussing the limitations and pitfalls of LLM evaluation, it’s important to distinguish between two related but distinct types of evaluations. There’s a fundamental difference between standardized, public benchmarks designed to measure general model capabilities and the custom, application-specific evaluations that determine whether a system actually meets your needs.
In this post, we’ll clarify the distinction between standardized LLM benchmarks and application-specific evals, and explore some of the most common pitfalls that teams encounter when interpreting eval results. Along the way, we’ll discuss why high leaderboard scores don’t always translate to real-world readiness, the challenges of using LLM-as-a-judge, the importance of statistical rigor at different stages of evaluation, and the need for continuous, evolving evals as your product and users change. By understanding these issues, you’ll be better equipped to design evaluation processes that align with your goals and avoid common traps that we’ve seen teams run into.
Standardized Benchmarks vs. Application-Specific Evals
When discussing benchmarks in the context of LLMs, we often refer to two related but distinct concepts: Standardized Model Benchmarks and Application-Specific Evals . These serve different purposes and have different implications for model development and deployment. Before addressing common misconceptions about LLM benchmarks, it’s important to distinguish between these two types. Throughout the following sections, we will specify which type of benchmark we are referencing whenever the distinction is relevant.
Standardized benchmarks test the core model’s general skills, while application-specific evaluations measure how the whole system performs on real tasks that matter to users.
Standardized LLM Benchmarks — like MMLU, GLUE, or HellaSwag - are shared, public datasets and tasks designed to measure the general capabilities of a core language model across a wide range of knowledge domains and skills. Their primary purpose is to enable fair comparisons between different foundation models and to track overall progress in the field. However, these benchmarks have significant limitations: they can suffer from data contamination (where test data leaks into training), may not reflect performance on the specific tasks that matter for your application, and can sometimes be “gamed” by optimizing models for benchmark performance rather than real-world utility.
Application-Specific Evals — in contrast, are tailored to the needs of a particular application or compound AI system. Rather than focusing on the core model in isolation, these evaluations measure how the entire system - including prompts, retrieval components, agents, and other integrations - performs on tasks directly relevant to the intended use case and the surrounding business logic. The goal here is not to compare models in the abstract, but to ensure the system meets user and business needs, uncover specific failure modes, and guide ongoing iterations. Application-specific evals often start small and curated, evolving and blending quantitative metrics with qualitative assessments to capture the nuances of real-world performance.
Common Pitfalls in LLM Evaluation
With that in mind, let's examine some common misconceptions and pitfalls when interpreting benchmark results and discuss how to approach evaluation in a way that aligns with your goals and use cases.
Mistaking Benchmark Scores for Real-World Readiness It's tempting to assume that high scores on standardized benchmarks automatically translate to good performance in real-world applications. However, a high score on a public benchmark doesn't guarantee that simply switching models will significantly improve your application performance. LLM Benchmarks are designed to measure the general capabilities of the core language model across a wide range of knowledge domains or reasoning tasks. While they help compare foundation models and track overall progress in the field, they come with significant limitations. For one, benchmarks can suffer from data contamination, where models have seen parts of the test set during training, artificially inflating their scores.
It's also important to mention the issue of benchmark saturation - as LLMs become more capable, it's not uncommon for the top ranks at a given benchmark to reach close to perfect scores. When this happens, the benchmark loses its usefulness, as it can no longer differentiate one model from the other. This is already the case for multiple benchmarks, such as HumanEval [1] and GSM8K [2], with the top models reaching over 90% overall performance. Benchmark saturation leads to the continuous need to release newer and harder versions for the saturated benchmarks or search other more challenging datasets.
Relying solely on these scores can lead to a false sense of security. A model that excels on a leaderboard may still stumble when faced with the messy, context-rich, and often ambiguous scenarios encountered in production. This is especially true when moving from evaluating the core LLM to assessing the entire system's performance, including prompts, retrieval components, agents, and other integrations.
Recent research highlights how leaderboard scores can be systematically distorted by selective reporting, private test sets, and unequal access to data[3]. In practice, high rankings may reflect overfitting to the benchmark rather than genuine improvements in real-world capability.
A single score isn’t enough Perhaps the most crucial aspect to consider is that you need more than a single benchmark score to act and improve upon your application. A real-world system often involves different model capabilities applied to a specific domain and other interactions with external tools, so replacing your current model with a possibly better one is usually just the start of your improvement process. Let's take an example of a recipe and nutrition system, where a user could ask things like:
"I’m looking for a healthy vegetarian dinner recipe that uses sweet potatoes and spinach. I’d like something under 500 calories per serving, and I’m allergic to nuts. Can you find a recipe for me, tell me the ingredients, and estimate the calories per serving?”
A higher score in a general reading comprehension benchmark doesn't tell you how the model will behave in context with all the remaining moving parts of your system. To do so, you need to decompose your system into smaller tasks. In this simple example, maybe this could mean something like:
Is the system calling the web search API with the proper query, based on the user prompt?
Is the system calling the web search API with the proper query, based on the user prompt? Is the web search returning relevant recipes? Are we selecting the best recipe out of all retrieved recipes? Did the system accurately extract data such as ingredients, quantities, and preparation steps into a structured JSON? Did the system correctly estimate the calories and other nutrition information from the structured data? Did the system correctly adapt the recipe according to the user's restrictions/preferences? Did the system correctly deliver a final recipe to the user?
A complex system, like a recipe and nutrition assistant, requires evaluation at each step. Only by breaking down and assessing each component can you identify where improvements are needed, rather than relying on a single overall benchmark score.
If only evaluating the final step, you won’t know why the response was unsuccessful - did we not retrieve the correct documents, was the ingredients parsing off, or perhaps we failed to adjust the recipe, even if all the upstream processes were correct? More granular evals mean more actionable insights to improve your system. Ultimately, while standardized benchmarks are a valuable tool, they are not a substitute for application-specific evals.
Overlooking the Limits of LLM-Based Evaluation Using LLMs as judges is attractive for several reasons. Unlike traditional metrics that require human-written reference texts, LLM judges can directly evaluate open-ended outputs, making it possible to assess tasks where references are scarce or subjective. LLM-based evaluation also scales far beyond what's feasible with human annotators, dramatically reducing costs and turnaround time.
Recent studies have shown that, in some cases, generalist LLMs can achieve high alignment with human evaluators, especially when using advanced models and carefully crafted evaluation prompts [4, 5]. This has led to growing enthusiasm for automating evaluation pipelines with LLMs in the loop.
However, it's essential to recognize this approach's limitations and potential pitfalls. First, if you use generalist LLMs through APIs, changes to the underlying models are often opaque, hurting the reproducibility of evaluation results. Second, the design of the evaluation process itself is critical.
Designing your evaluation process
Achieving reliable results requires careful prompt engineering and clear, unambiguous evaluation criteria. This will require some experimentation for your particular use case, but here are some general recommendations for designing your evaluation prompt:
Provide a clear definition of your task, evaluation criteria, and adopted scale. Don’t boil the ocean with a single LLM-as-a-judge call that evaluates multiple criteria; instead, break it down into multiple yes/no type evals. A Likert scale, such as asking the LLM to output a score between 1 and 5, can be confusing to interpret. When possible, prefer binary pass/fail judgements, which are easier to interpret and align with human judgement [6]. Adding few-shot examples of questions in your evaluation prompt can improve your results, like in other LLM tasks. Studies have shown that pairwise comparisons have better correlations with human preferences than direct scoring, for example, asking the judge to “Evaluate and compare the coherence of the two following summaries, ” rather than asking it to “Evaluate the coherence of the following summary ” [7]. As in other LLM tasks, chain-of-thought style reasoning can improve performance by asking the model to output its reasoning before generating the score [8].
Evaluate your evaluators
Crucially, it's essential to evaluate your evaluators. To do so, the role of the domain expert is key, so you can always have a baseline to compare your evaluator's outputs to. This is also helpful in tuning your evaluation prompt, as this process is very circular - you need clear evaluation criteria to be able to grade your outputs, but the act of grading helps you more clearly define those criteria [9]. You don't need your domain expert to evaluate all the outputs judged by your evaluator, which would defeat the purpose of using LLMs as a judge, but you can curate a sample of representative examples, and use it to align your automated judge with your baseline continuously. Based on the overall agreement, you can tweak your judge's different parameters, as seen in the previous section, and iterate on your experiments to improve the correlation. Don't expect it to reach 100%, though - LLMs are stochastic, and so are humans.
Evaluating your evaluators is an ongoing process, so it's essential to set up a process that facilitates your domain expert's collaboration. For example, it should be easy to browse through the examples, all the required information should be centralized, and the annotations should be easily discoverable by the rest of the team.
Other limitations and biases
LLM judges also inherit the well-known limitations of LLMs in general. They can hallucinate, make factual errors, or struggle to follow complex instructions- issues that can directly impact the quality and reliability of their evaluations. Studies have found that LLM judges may exhibit systematic biases, such as favoring LLM-generated outputs over high-quality human-written text [4], or assigning scores that diverge significantly from those given by human annotators, even on relatively simple tasks[5]. This is not an exhaustive list - the table below illustrates additional biases that can influence evaluation outcomes.
Definitions of different cognitive biases, with examples. Source: Benchmarking Cognitive Biases in Large Language Models as Evaluators [10].
In short, LLM-as-a-judge evaluation is a powerful tool for scaling up assessment and reducing reliance on costly human annotation. Still, it is not a perfect substitute for human judgment. Over-reliance on automated evaluation can mask important failure modes, introduce new biases, and ultimately lead to overconfidence in system performance. Human-in-the-loop evaluation remains essential for critical applications, and LLM-based judgments should always be interpreted with appropriate caution and context.
Misapplying Statistical Rigor Across Evaluation Stages When evaluating LLMs, it’s essential to recognize that not all evaluation stages require the same level of statistical rigor. The goals and appropriate methods differ depending on whether you're running quick, custom tests, or reporting results from standardized benchmarks.
In early-stage, custom evaluations, it's common - and often necessary - to work with small, hand-picked datasets. Here, the goal is to identify failure modes, spot qualitative issues, or iterate quickly on model behavior. Demanding strict statistical significance at this stage can be counterproductive, as the focus is on exploration rather than precise measurement. For example, if you're just trying to find out whether your model can handle a new type of prompt, running a handful of examples is often enough to guide further development.
However, the need for statistical rigor increases as evaluations mature, especially when comparing models or reporting results on standardized benchmarks. Here, the goal shifts to making reliable, quantitative claims about model performance. This is where issues like dataset size, the number of evaluation runs, and the inherent non-determinism of LLMs become critical. LLMs can produce different outputs for the same input due to their probabilistic nature, so results from a single run or a small dataset may not reflect true performance. Without sufficient data or repeated measurements, you risk being misled by random fluctuations - a classic case of "the curse of small numbers" [11].
It's also important to recognize that the transition from qualitative to quantitative evaluation is not a sharp line, but depends on your goals, the maturity of your system, and the decisions you intend to make based on the results. For instance, if you're deciding whether to ship a new model version based on benchmark results, statistical rigor is essential. But a handful of examples may suffice if you're just trying to identify new error types.
The main pitfall is either demanding statistical significance too early, wasting effort on exploratory tests, or failing to apply it when it matters, leading to unreliable or misleading comparisons. Understanding the purpose of your evaluation at each stage helps avoid both traps.
The Case for Continuous Evaluation
A common pitfall in LLM benchmarking is treating custom evaluation as a one-time task, relying on static offline datasets that quickly become outdated as your product and its users evolve. The real world is a very dynamic environment; along with it, your users' behaviors also change over time. That means your input data experience distribution shifts, usage patterns change, user expectations evolve, and new failure modes emerge. The direct consequence is that your evaluation dataset must accompany this shift. Otherwise, it will fail to catch regressions and miss new vulnerabilities. This phenomenon is known as Dataset Drift , but it's also not the only type of drift you should be aware of.
Not only should your dataset be continuously updated, but your evaluation criteria also evolve with time. To grade your outputs, you need clear evaluation criteria, but the process of grading often helps you to iterate and improve on your evaluation criteria. In the paper by Shankar et al., “Who Validates the Validators?" , it was observed that, when grading the outputs of LLM-as-a-judge evaluators, human graders would often refine their criteria when observing new types of incorrect LLM outputs, or adjust the criteria to fit better the LLMs behavior, which they call Criteria Drift .
The bottom line is that you need to continuously iterate on your evaluations, updating your datasets and refining your evaluation criteria. Your evals should be treated as living documents, regularly updated in response to observed failures, changing requirements, and new user interactions. Here are some general recommendations:
Incorporate insights from production: Log your interactions, curate and label interesting or problematic cases, and feed them back into your evaluation datasets. Continuously measure the alignment of your automated evaluators against domain expert critiques to improve your evaluators over time. Iterate on your LLM evaluator prompts/evaluation criteria and test them against your evaluation datasets. Given the sheer volume of production data, running evaluations on a sample of your data can be helpful, especially when using LLM-as-a-judge metrics or human evaluation, where costs can become prohibitive.
The most effective evaluation processes close the loop between offline and online data, creating a continuous feedback cycle. This ongoing iteration ensures that your benchmarks remain relevant and your system is tested against the real challenges it faces in the wild. The insights and visibility gained from your tuned custom evals will guide you on what changes you need to make to improve your application, be it fine-tuning your models, changing your prompts, or adjusting any other step in your pipeline.
Illustration of the continuous evaluation cycle: as user behavior and product requirements evolve, your custom evals must be regularly updated to catch regressions, address new failure modes, and ensure benchmarks remain relevant in dynamic real-world environments.
The most successful LLM teams treat evaluation as a living, iterative practice. They blend quantitative and qualitative methods, continuously update their test sets with real user data, and maintain a healthy skepticism toward any single metric or approach. By grounding your evaluation strategy in the realities of your application and remaining vigilant to new challenges as they arise, you can build LLM systems that are impressive on paper and truly robust, reliable, and valuable in the hands of your users.
References Code Generation on HumanEval - Leaderboard Arithmetic Reasoning on GSM8K - Leaderboard Singh, Shivalika, et al. "The Leaderboard Illusion." arXiv preprint arXiv:2504.20879 (2025). Liu, Yang, et al. "G-Eval: NLG evaluation using GPT-4 with better human alignment (2023)." arXiv preprint arXiv:2303.16634 12 (2023). Thakur, Aman Singh, et al. "Judging the judges: Evaluating alignment and vulnerabilities in llms-as-judges." arXiv preprint arXiv:2406.12624 (2024). Hamel Husain - Creating a LLM-as-a-Judge That Drives Business Results Liu, Yinhong, et al. "Aligning with human judgement: The role of pairwise preference in large language model evaluators." arXiv preprint arXiv:2403.16950 (2024). Bai, Yuntao, et al. "Constitutional ai: Harmlessness from ai feedback, 2022." arXiv preprint arXiv:2212.08073 8.3 (2022). Shankar, Shreya, et al. "Who validates the validators? aligning llm-assisted evaluation of llm outputs with human preferences." Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology . 2024. Koo, Ryan, et al. "Benchmarking cognitive biases in large language models as evaluators, 2023." URL https://arxiv . org/abs/2309.17012 (2023).Kamilė Lukošiūtė - You need to be spending more money on evals.
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