How to evaluate the quality of your Large Language Model output before deploying

Perhaps you’ve been experimenting with GenAI for some time now, but how do you determine when the output quality of your Large Language Model (LLM) is sufficient for deployment? Of course, your solution needs to meet its objectives and deliver reliable results. But how can you evaluate this effectively?

In contrast to LLMs, assessing machine learning models is often a relatively straightforward process: metrics like Area Under the Curve for classification or Mean Absolute Percentage Error for regression give you valuable insights in the performance of your model. On the other hand, evaluating LLMs is another ball game, since GenAI generates unstructured, subjective outputs – in the form of texts, images, or videos - that often lack a definitive "correct" answer. This means that you’re not just assessing whether the model produces accurate outputs; you also need to consider, for example, relevance and writing style.

For many LLM-based solutions (except those with very specific tasks, like text translation), the LLM is just one piece of the puzzle. LLM-based systems are typically complex, since they often involve multi-step pipelines, such as retrieval-augmented generation (RAG) or agent-based decision systems, where each component has its own dependencies and performance considerations.

In addition, system performance (latency, cost, scalability) and responsible GenAI (bias, fairness, safety) add more layers of complexity. LLMs operate in ever-changing contexts, interacting with evolving data, APIs, and user queries. Maintaining consistent performance requires constant monitoring and adaptation.

With so many moving parts, figuring out where to start can feel overwhelming. In this article, we’ll purposefully over-simplify things by answering the question: “How can you evaluate the quality of your LLM output?”. First, we explain what areas you should consider in the evaluation of the output. Then, we’ll discuss the methods needed to evaluate output. Finally, to make it concrete, we bring everything together in an example.

What are the evaluation criteria of LLM output quality?

High-quality outputs build trust and improve user experience, while poor-quality responses can mislead users and foster misinformation. The start of building an  evaluation (eval) is to start with the end-goal of the model. The next step is to define the quality criteria to be evaluated. Typically these are:

• Correctness: Are the claims generated by the model factually accurate?
• Relevance: Is the information relevant to the given prompt? Is all required information provided -by the end user, or in the training data- to adequately offer an answer to the given prompt?
• Robustness: Does the model consistently handle variations and challenges in input, such as typos, unfamiliar question formulations, or types of prompts that the model was not specifically instructed for?
• Instruction and restriction adherence: Does the model comply with predefined restrictions or is it easily manipulated to jailbreak the rules?
• Writing style: Does the tone, grammar, and phrasing align with the intended audience and use case?

How to test the quality of LLM outputs?

Now that we’ve identified what to test, let’s explore how to test. A structured approach involves defining clear requirements for each evaluation criterion listed in the previous section. There are two aspects to this: references to steer your LLM towards the desired output and the methods to test LLM output quality.

1. References for evaluation

In LLM-based solutions, the desired output is referred to as the golden standard, which contain reference answers for a set of input prompts. Moreover, you can provide task-specific guidelines such as model restrictions and evaluate how well the solution adheres to those guidelines.

While using a golden standard and task-specific guidelines can effectively guide your model towards the desired direction, it often requires a significant time investment and may not always be feasible. Alternatively, performance can also be assessed through open-ended evaluation. For example, you can use another LLM to assess relevance, execute generated code to verify its validity, or test the model on an intelligence benchmark.

2. Methods for assessing output quality

Selecting the right method depends on factors like scalability, interpretability, and the evaluation requirement being measured. In this section we explore several methods, and assess their strengths and limitations.

2.1. LLM-as-a-judge

An LLM isn’t just a text generator—it can also assess the outputs of another LLM. By assessing outputs against predefined criteria, LLMs provide an automated and scalable evaluation method.

Let’s demonstrate this with an example. For example, ask the famous question, "How many r's are in strawberry?" to ChatGPT's 4o mini model. It responds with, "The word 'strawberry' contains 1 'r'.", which is obviously incorrect. With the LLM-as-a-judge method, we would like the evaluating LLM (in this case, also 4o mini) to recognize and flag this mistake. In this example, there is a golden reference answer “There are three 'r’s'  in 'strawberry'.”, which can be used to evaluate the correctness of the answer.

Indeed, the evaluating LLM appropriately recognizes that the answer is incorrect.

The example shows that LLMs can evaluate outputs consistently and at scale due to their ability to quickly assess several criteria. On the other hand, LLMs may struggle to understand complex, context-dependent nuances or subjective cases. Moreover, LLMs may strengthen biases within the training data and can be costly to use as an evaluation tool.

2.2. Similarity metrics for texts

When a golden reference answer is available, similarity metrics provide scalable and objective assessments of LLM performance. Famous examples are NLP metrics like BLEU and ROUGE, or more advanced embedding-based metrics like cosine similarity and BERTScore. These methods provide quantitative insights in measuring the overlap in words and sentence structure without the computational burden of running full-scale LLMs. This can be beneficial when outcomes must closely align with provided references – for example in the case of summarization or translation.

While automated metrics provide fast, repeatable, and scalable evaluations, they can fall short on interpretability and often fail to capture deeper semantic meaning and factual accuracy. As a result, they are best used in combination with human evaluation or other evaluation methods.

2.3. Human evaluation

Human evaluation provides a strong evaluation method due to its flexibility. In early stages of model development, it is used to thoroughly evaluate errors such as hallucinations, reasoning flaws, and grammar mistakes to provide insights into model limitations. As the model improves through iterative development, groups of evaluators can systematically score outputs on correctness, coherence, fluency, and relevance. To reduce workload and enable real-time human evaluation after deployment, pairwise comparison can be used. Here, two outputs are compared to determine which performs better for the same prompt. This is in fact implemented in ChatGPT.

It is recommended to use both experts as non-experts in human evaluation of your LLM. Experts can validate the model’s approach based on their expertise. On the other hand, non-experts play a crucial role in identifying unexpected behaviors and offering fresh perspectives on real-world system usage.

While human evaluation offers deep, context-aware insights and flexibility, it is resource- and time-intensive. Moreover, comparing different examiners can lead to inconsistent evaluations when they are not aligned.

2.4. Benchmarks

Lastly, there are standardized benchmarks that offer an approach to assess the general intelligence of LLMs. These benchmarks evaluate models on various capabilities, such as general knowledge (SQuAD), natural language understanding (SuperGLUE), and factual consistency (TruthfulQA). To maximize their relevance, it’s important to select benchmarks that closely align with your domain or use case. Since these benchmarks test broad abilities, they are often used to identify an initial model for prototyping. However, standardized benchmarks can provide a skewed perspective due to their lack of alignment with your specific use case.

2.5. Task specific evaluation

Depending on the task, other evaluation methods are appropriate. For instance, when testing a categorization LLM, accuracy can be measured using a predefined test set alongside a simple equality check (of the predicted category vs. actual category). Similarly, the structure of outputs can be tested by counting line-breaks; certain headers and/or the presence of certain keywords can also be checked. Although these technique are not easily generalizable across different use cases, they offer a precise and efficient way to verify model performance.

Putting things together: writing an eval to measure LLM summarization performance

Consider a scenario where you're developing an LLM-powered summarization feature designed to condense large volumes of information into three structured sections. To ensure high-quality performance, we evaluate the model for each of our five evaluation criteria. For each criterion, we identify a key question that guides the evaluation. This question helps define the precise metric needed and determines the appropriate method for calculating it.

Criterium	Key question	Metric	How
Correctness	Is the summary free from hallucinations?	Number of statements in summary that can be verified based on source text	* Use an LLM-as-a-judge to check if each statement can be answered based on the source texts * Use human evaluation to verify correctness of outputs
Relevance	Is the summary complete?	Number of key elements present with respect to a reference guideline or golden standard summary	Cross-reference statements in summaries with LLM-as-a-judge and measure the overlap
	Is the summary concise?	Number of irrelevant statements with respect to golden standard Length of summary	* Cross-reference statements in summaries with LLM-as-a-judge and measure the overlap * Count the number of words of generated summaries
Robustness	Is the model prone to noise in the input text?	Similarity of summary generated for original text with respect to summary generated for text with noise such as typo’s and inserted irrelevant information	Compare statements with LLM-as-a-judge, or compare textual similarity with ROUGE or BERTscore
Instruction & restriction adherence	Does the summary comply with required structure?	Presence of three structured sections	Count number of line breaks and check presence of headers
Writing style	Is the writing style professional, fluent and free of grammatical errors?	Rating of tone-of-voice, fluency and grammar	* Ask LLM-as-a-judge to rate fluency and professionality and mark grammatical errors * Rate writing style with human evaluation
Overarching: alignment with golden standard	Do generated summaries align with golden standard summaries?	Textual similarity with respect to golden standard summary	Calculate similarity with ROUGE or BERTscore

The table shows that the proposed evaluation strategy leverages multiple tools and combines reference-based and reference-free assessments to ensure a well-rounded analysis. And so we ensure that our summarization model is accurate, robust, and aligned with real-world needs. This multi-layered approach provides a scalable and flexible way to evaluate LLM performance in diverse applications.

Final thoughts

Managing LLM output quality is challenging, yet crucial to build robust and reliable applications. To ensure success, here are a few tips:

• Proactively define the evaluation criteria. Establish clear quality standards before model deployment to ensure a consistent assessment framework.
• Automate when feasible. While human evaluation is essential for subjective aspects, automate structured tests for efficiency and consistency.
• Leverage GenAI to broaden your evaluation. Use LLMs to generate diverse test prompts, simulate user queries, and assess robustness against variations like typos or multi-language inputs.
• Avoid reinventing the wheel. There are already various evaluation frameworks available on the internet (for instance, DeepEval). These frameworks provide structured methodologies that combine multiple evaluation techniques.

Achieving high-quality output is only the beginning. Generative AI systems require continuous oversight to address challenges that arise after deployment. User interactions can introduce unpredictable edge cases, exposing the gap between simulated scenarios and real-world usage. In addition, updates to models and datasets can impact performance, making continuous evaluation crucial to ensure long-term success. At Rewire, we specialize in helping organizations navigate the complexities of GenAI, offering expert guidance to achieve robust performance management and deployment success. Ready to take your GenAI solution to the next level? Let’s make it happen!

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This article was written by Gerben Rijpkema, Data Scientist at Rewire, and Renske Zijm, Data Scientist at Rewire.