LLM Evaluation: Key Concepts & Best Practices

Evaluation of large language models (LLMs) is a crucial step in deploying the models in a real-world environment. This requires going beyond simple accuracy metrics to assess relevance, reliability, and task completion in specific use cases. 

This article discusses the key factors to consider and best practices in LLM evaluation. 

Summary of key factors in LLM evaluation 

Understanding LLM evaluation

LLM evaluation is a structured process that assesses an LLM’s performance across various tasks and capabilities, including accuracy, relevance, and reliability. The goal is to validate whether it meets the requirements of a specific use case, such as answering queries, writing code, or document summarization.  LLM evaluation also prevents dependence on expensive models because high-performing models often come with high operational costs. Organizations can select a more cost-effective model tailored to their specific use cases.

LLM evaluation involves separate quantitative metrics and qualitative analysis. Standardizing evaluation metrics is beneficial in selecting the right model for your specific needs. 

LLM evaluation strategies

Evaluation strategies can be either human-based, automatic, or a mixture of both. Two typical startegies used to verify the output of LLMs are explained below. 

Gold reference comparison

This method compares the LLM’s output with a human-curated verified answer, or a “gold reference” created by human experts. This accuracy-based evaluation can be helpful for tasks like code generation and summarization. Once you have a gold reference and the model’s output, several strategies can be used to compare them automatically and generate scores. A few such strategies are explained below: 

BLEU (Bilingual Evaluation Understudy): This traditional method was developed initially for machine translation. It measures the sequences of contiguous words between the text generated by the LLM and the reference. High scores mean more similarity to references, and these scores range from 0 to 1. BLEU is sometimes unsuitable because it may invalidate outputs based on different wording. Due to its inconsistent performance, BLEU is no longer widely used.

ROUGE: Recall-Oriented Understudy for Gisting Evaluation (ROUGE) is similar to BLEU. However, it focuses on recall, meaning it measures the overlap in word sequences. In other words, it compares the extent to which the model’s output contains the reference content. It is popular for summarizing tasks where including all the relevant elements is necessary. ROUGE is less commonly used because it focuses on surface-level text, which does not capture the semantic quality.

Embedding similarity scores: The score measures the semantic similarity between a model’s generated output and a reference text by comparing their vector representations or embeddings rather than relying on exact word matches. It is a conceptual-level measure, rather than similar-word checking. Cosine similarity is one of the metrics used to measure embedding similarity scores. It offers a more semantic judgment where two sentences can appear different but convey the same meaning.

Model-based scores

In Model-based scores, instead of relying on human references, we can check the responses using another LLM as a judge.

Statistical approaches are still helpful, but they struggle with creative or open-ended tasks where different results have equal validity. These challenges motivated the development of model-based evaluation techniques, in which strong LLM models act as judges. AI is utilized to assess AI.

Note: Using models as evaluators carries a risk of bias, especially when the judge and the subject model share the same architecture or training data. This can lead to overly favorable or skewed evaluations.

G-Eval: G-Eval (General Evaluation) is a technique for scoring defined metrics and evaluating the overall performance of the LLM using LLM as a judge approach. Instead of human annotators and traditional metrics like BLEU or ROUGE, G-Eval prompts another LLM and scores various metrics. As the LLM defines the scoring criteria, consistency is ensured, and a significant amount of time is saved. Rather than a simple binary output as pass or fail, G-Eval returns a score for all dimensions. All these scores can be averaged to get the ultimate performance score.

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LLM evaluation metrics

Use case-specific metrics 

Use case-specific metrics ensure the LLM evaluation meets the application’s goals and considers the risks associated with the application. This is important because LLM performance varies significantly across use cases. An LLM that performs well in a chat assistant use case may not work well in a document processing use case. Use case specific metrics aligns model performance with business objectives, enhances reliability, and mitigates domain-specific issues that generic metrics might overlook. The following are some well-known use-case-specific metrics. 

Answer relevancy

The answer relevance metric assesses how well the LLM’s output aligns with the given input. Comparing the output to the provided input ensures that the LLM’s responses remain focused, relevant, and valuable. The score is calculated according to the following equation.

For example, for the prompt “Explain the benefits of green tea?” the ideal response is “Green tea is rich in antioxidants. It supports weight loss and improves brain function.”

However, consider the LLM response: “Green tea tastes slightly bitter and is popular in many cultures. It is often consumed in Japan and China.” This response contains two statements, and neither is relevant, so the answer’s relevance score is  0/2 = 0

Consistency

Consistency reflects an LLM’s ability to repeatedly arrive at the same responses over the same input parameters.  It evaluates whether the model can reliably produce similar responses across multiple runs, which is important for developing its behavioural trustworthiness. It is helpful in applications where deterministic outputs are critical, such as legal document generation, financial reporting, or customer service responses, where variability could lead to confusion, errors, or compliance risks.

To compute the consistency of an LLM, provide prompts to the LLM n times and observe the responses. You can set the value of the temperature parameter higher to allow some variance in the response. Use a strong LLM as a judge to determine response consistency. 

The consistency score is computed as follows:

For example, consider the prompt, “What are flu symptoms?” gives the following responses in consecutive runs:

• “Fever, cough, sore throat, body aches”
• “Fever, sore throat, cough, and muscle pain.” 

As both responses are semantically consistent, consistency scores would be 1/1 = 1

Faithfulness/hallucination

Faithfulness ensures that the LLM’s output is factually accurate according to the given context. It provides factual consistency by penalizing claims that cannot be directly derived or logically inferred from the given context. It helps prevent hallucinations and maintains the reliability of generated outputs. It is computed using the following equation.

For example, let’s say an LLM has been given the prompt, “Summarize Einstein’s contributions to physics based on the provided text,” and the context, “Albert Einstein proposed the special theory of relativity in 1905. He later developed the general theory of relativity in 1915.” The LLM response is: “Einstein developed the special theory of relativity in 1905 and the general theory in 1915. He won the Nobel Prize in 1921 for his work on relativity.” 

Two of the claims are correct, while the Nobel Prize claim is not in context and is factually inaccurate. The Faithfulness score, in this case, would be 2/3.

JSON correctness

JSON correctness measures the ability of an LLM model to produce responses that adhere to valid JSON syntax and format, allowing for seamless parsing and integration with downstream systems. It is crucial for tasks where LLMs generate machine-readable responses, such as API calls or automation workflows. 

Even if the content is accurate, improperly formatted JSON can lead to failures. Therefore, JSON correctness is a key evaluation metric to assess the reliability and usability of LLM-generated structured data. 

It is possible to automatically validate JSON integrity using tools such as Python’s json module.loads() function, which will parse a string as a JSON object and raise an error if the format is incorrect. Libraries such as AJV in JavaScript or online validation tools like JSONLint also assist in automating and normalising LLM-created JSON output validation.

Note: Lower temperature settings yield more consistent and predictable LLM outputs, ideal for real-world deployments where reliability is crucial. Higher temperatures add variability but reduce consistency.

For example, the following JSON is valid.

{
  "name": "Abc",
  "age": 30,
  "skills": ["Python"]
}

Let’s say, an LLM provides the following with a missing comma after ‘Alice’, and the age is represented as a string instead of a number.

{
  "name": "Abc"
  "age": thirty,
  "skills": ["Python"]
}

In that case, the score will be 0.

Tool correctness

It is an agentic LLM metric that measures an LLM agent’s ability to accurately invoke external tools, APIs, and functions. With the increasing integration of LLM models into complex systems, it is insufficient to generate only accurate responses; instead, agents must trigger the right tool based on user queries. This is particularly critical for domains where AI assistants trigger different workflows or automate tasks. It ensures reliability in real-world applications and prevents system failures due to incorrect tool usage. It is computed using the following equation.

For example, an LLM agent calling the tool summarize() instead of translate() for a translation task would score 0/1 = 0.

Task completion

It is another agentic LLM metric that measures the ability of an LLM agent to complete a task defined by a user through an input command. It assesses the tool usage and final output response to determine successful task completion. The score is computed as follows: We analyze input, output, and tool usage to determine if the outcome is relevant to the task. 

Task Completion Score = AlignmentScore(Task, outcome)

For example, for the task “Find today’s weather in New York and summarize whether I should carry an umbrella.”, an LLM agent calls the correct tools, such as get_weather(location) and summarize_weather(data), and returns “Yes, you should carry an umbrella. There is a high chance of rain today in New York.” So, the task completion score is 1.

Generic LLM evaluation metrics

To evaluate LLM performance across various general  tasks, including knowledge understanding, logical reasoning, natural language understanding, and robustness, we can utilize the following benchmarks. 

MMLU

The Massive Multitask Language Understanding1 benchmark measures the general knowledge and reasoning ability of an LLM across 57 diverse subjects, including science, mathematics, history, law, humanities, and more, using multiple-choice questions. MMLU evaluates a model’s capability to apply knowledge and reasoning across diverse domains, helping to solve complex tasks that require broad knowledge and contextual understanding. 

ARC

AI2 reasoning challenge2 benchmark measures language models’ reasoning skills. It uses 8,000 multiple-choice science questions from grades 3 to 9. These questions typically go beyond basic information retrieval and require more comprehension and logical reasoning to answer correctly. The benchmark offers two modes: Easy and Challenge, the latter containing more complex questions that demand advanced reasoning capabilities. 

GLUE

General Language Understanding Evaluation (GLUE)3 benchmarks assess an LLM’s core language understanding and contextual comprehension through nine complex NLP tasks. These tasks include sentiment analysis, text similarity, paraphrase detection, natural language inference, and more.

HELM

Holistic Evaluation of Language Models (HELM)4 evaluates LLM performance across various tasks and evaluation metrics, including accuracy, robustness, fairness, efficiency, and others. The framework limits the ability of LLMs to generate harmful content. The comprehensive assessment ensures that LLM models are robust, safe, and versatile. 

LLM evaluation frameworks

LLM Evaluation frameworks provide a set of reusable functions and metric implementations that can be used for evaluating the metrics discussed above. The following section details some of the popular LLM evaluation frameworks.

DeepEval

The DeepEval framework features over 30 evaluation metrics, including RAG, agentic, and conversational matrices, which assess faithfulness, relevance, bias, and toxicity, and provide interpretable feedback. It allows teams to rapidly diagnose where a model’s performance may be lacking. 

Modular design is one of DeepEval’s standout features, which allows for easy combinations of existing metrics or the development of custom ones to suit specific use cases. The framework treats evaluations like unit tests, with built-in Pytest support enabling developers to directly integrate LLM checks into their existing workflows.

It also supports the creation of synthetic datasets to test edge cases and provides flexible data-loading options from CSVs, JSON files, and Hugging Face hubs. DeepEval offers a hosted version with a free tier for teams requiring real-time evaluation capabilities, making it a cost-effective and developer-friendly option for robust LLM evaluation. The following code snippet demonstrates how to utilize DeepEval to calculate answer relevance.

from deepeval import evaluate
from deepeval.metrics import AnswerRelevancyMetric
from deepeval.test_case import LLMTestCase


# Define your inputs
question = "What is the capital of France?"
actual_output = "The capital of France is Paris."


# Create a test case
test_case = LLMTestCase(
    input=question,
    actual_output=actual_output
)

# Initialize the Answer Relevancy metric
metric = AnswerRelevancyMetric(threshold=0.7, model="gpt-4-turbo", include_reason = True) 

# Run the evaluation
metric.measure(test_case)
print(metric.score, metric.reason)

The code evaluates the relevance of the model’s response to the input question, using the GPT model as the judge. It prints a score (from 0 to 1) and a reasoning explanation that justifies the score. The following output from the code demonstrates how DeepEval generates interpretable feedback.

Output of the code: 

1.0   The score is 1.00 because the response accurately and directly answered the question without any irrelevant information.

TruLens

TruLens is an open-source platform that uses feedback functions to assess the quality and performance of LLM-based applications. A feedback function evaluates the output of an LLM application by determining both the generated text and associated metadata from the LLM. 

Feedback functions facilitate the automated evaluation of inputs, outputs, and intermediate steps, enabling teams to accelerate and scale their experimentation processes. The functions allow LLM evaluation across many critical areas, such as context relevance, groundedness, answer relevance, comprehensiveness, harmful or toxic language detection, user sentiment, language consistency, fairness, and bias monitoring, and custom feedback options. 

TruLens supports many use cases, including question answering, summarization, retrieval-augmented generation (RAG), and agent-based systems.

OpenAI Evaluations

OpenAI Evals is an open-source LLM evaluation framework for developing and executing custom evaluations. 

Its design encourages adaptability, making it a strong choice for developers looking to tailor tests to specific use cases. The project’s community-driven nature also promotes knowledge sharing, with contributors regularly adding new evaluation techniques and benchmarks for diverse application domains.

Functionally, OpenAI Evals operates by combining user-defined prompts, scoring functions, and post-processing scripts to assess model performance. The modular architecture facilitates the building and reuse of evaluation workflows, enabling the integration of tasks such as compliance checks into the larger model development process. 

Its high degree of customizability makes it especially beneficial for engineering teams that value flexibility and tight integration with their existing toolchains.

Recommendations

LLM evaluation is complex process that requires several iterations to get right. It is a key success factor while implementing a production-grade Gen AI application. We recommend the following structured and practical approaches to evaluate an LLM.

Leverage LLMOps tools and workflows

LLMOps frameworks help systematically evaluate and benchmark LLMs and LLM-based applications. Popular data integration tools also support automated comparison and evaluation of LLMs as part of data pipelines. 

Tools like Nexla provide robust pipelines to handle automated data transformation and validation and deliver clean and trustworthy evaluations. These frameworks enable organizations to integrate evaluation into their development workflows, simplifying the process of testing large language models in repeatable and scalable ways. With the inclusion of automated verification for JSON correctness, answer relevancy, and hallucination detection, these frameworks minimize the risk of deploying poorly performing models into production.

Incorporate multiple evaluation metrics

We recommend using several evaluation metrics to assess LLMs. The practice includes reviewing the clarity, accuracy, relevance, and fluency of responses. Using metrics such as perplexity, consistency, and task completion provides a balanced view of model performance. Assessing along various dimensions enables teams to identify specific shortcomings and adapt models for improved dependability. Using human judgement wherever possible is important to ensure the evaluation is bias free. 

Use real-world scenarios in testing

We recommend testing models in real-world usage to represent performance as closely as possible to practical scenarios, rather than idealized laboratory conditions. This includes testing with ambiguous questions, missing input, domain-specific tasks (e.g., legal summarization or API calls), and even adversarial or vague prompts. Tools such as DeepEval facilitate this process through synthetic test case generation, allowing teams to programmatically simulate a range of user inputs and stressful conditions. 

This methodology helps measure how well the model performs under unforeseen user behavior, retains its strength, and aligns with actual business requirements. Testing under such circumstances ensures that the model is benchmark-strong, deployable, practical, and resilient.

Utilize evaluation frameworks for scalability

Frameworks like DeepEval, TruLens, and OpenAI Evals enable structured, scalable, and automated evaluation workflows. They support human, automatic, or hybrid evaluation approaches and have plugins for various metrics, such as semantic similarity and toxicity detection. They are modular, not only accelerating experimentation but also enabling continuous monitoring and quality assurance in production systems.

Look beyond aggregate metrics

With clear metrics defined for general and use case-specific evaluation, it is easy to get distracted by the aggregate-level performance of LLMs. With Gen AI being used in many high-risk fields like finance, healthcare, etc, it is important to go one step beyond aggregate-level metrics and identify failure modes within the test set. Even with a great aggregate score, the LLM may still be unusable for a task if it provides an incorrect response for a key scenario. Hence, it is important to subcategorize or group your test sets according to severity and compute metrics separately to identify specific failure modes. 

Last thoughts

With the increasing integration of LLMs into various applications, the need for systematic and multidimensional evaluation has never been more critical. Evaluation helps understand how well a model aligns with task-specific goals, user expectations, and operational constraints. 

Frameworks such as DeepEval, TruLens, and OpenAI Evals enable developers and organizations to evaluate LLM performance according to their specific business needs. As LLMs are increasingly integrated into mission-critical workflows, standardized and context-aware evaluation will be crucial to ensuring their reliability, safety, and practical utility in the real world.

 Data integration platforms like Nexla help integrate LLM evaluation workflows in data transformation pipelines. You can read more about Nexla and its support for LLM integration here.