The purpose of evaluation metrics is to translate a subjective ‘good’ or ‘bad’ into an objective figure. Instead of vague feedback like ‘I think the model has improved,’ you can state that ‘the quality score has increased from 73% to 81%.’

The problem is that language models generate text, and text is difficult to evaluate. If a model predicts a stock price, it’s easy to verify whether the prediction came true. If a model generates an answer to a client’s question, how can you tell if it’s a good or bad answer?

The correct answer can be formulated in dozens of ways; for that, you’ll definitely need an LLM evaluation metrics list. Completeness is important, but excessive detail is annoying. Factual accuracy is critical, but so is tone. A model can produce a contextually useless and technically correct answer.

To capture this nuance, you cannot rely on a single score; instead, you need a combination of metrics – LLM chatbot evaluation metrics that evaluate the model across different dimensions.

Why LLM assessment is more important than ever

The more tasks companies delegate to models, the higher the risks, from legal issues to reputational ones. For example, a model may produce “confident nonsense”, miss toxic wording, or incorrectly summarize documents, all of which go unnoticed at the initial stage. Businesses need to understand that a model that hasn’t been properly assessed becomes a risk, not an asset.

There’s also the issue of models changing. A provider releases a new API version, you update, and suddenly something goes wrong. Without a solid evaluation pipeline, you’ll learn about it from customer complaints. With metrics, you spot the problem before it reaches users.

The third reason: optimization costs efficiency. Queries to top models are expensive. You can use a cheaper model for some tasks, but you need to ensure that quality doesn’t drop dramatically. Without metrics, it’s a gamble.

The fourth: compliance and risks. In regulated industries (finance, healthcare, law), you need to prove that the model works correctly, is not hallucinatory, and does not discriminate. Comprehensive evaluation reports provide the necessary audit trail.

There are two fundamentally different approaches to evaluating LLM agent evaluation metrics: relying on standardized benchmarks or using judgment-based methods (such as human or AI review).

Benchmark evaluation

The idea is simple: you have the correct answer and the model’s answer; you compare them and see how similar they are.

For example, you’re testing a translation model. You have a sentence in English and a professional translation into Ukrainian. The model produces its translation, and you compare it to the benchmark. The closer it is to the benchmark, the better.

This approach works when the correct answer is known in advance and relatively unambiguous. Translation, info extraction, and answering factual questions with a specific correct answer are examples.

Judgment evaluation

What if there’s no correct answer, or there might be many? What if you’re generating creative copy, campaign ideas, or personalized recommendations?

This is where judgment evaluation comes in. You look at the model’s response and evaluate it based on specific criteria: consistency, completeness, tone, compliance with instructions, and factual accuracy.

LLM-as-a-Judge

You give the judge model a prompt like this:

“Rate the following answer on a scale of 1 to 5 based on the following criteria: factual accuracy, completeness, and clarity. Explain your rating.”

The judge model analyzes the response and assigns a score with justification.

Advantage: it can evaluate complex aspects that are difficult to formalize into rules. Disadvantage: the judge model can also make mistakes, and its scores are not always stable.

It’s important to note that the judge model must be more powerful than the model being evaluated. If you use GPT-3.5 to evaluate GPT-4, the results will be questionable. Typically, a top-tier model (GPT-4, Claude 3 Opus) is used as a judge.

Deterministic and rule-based approaches

Sometimes, LLM metrics evaluation criteria can be formalized as rules or code.

For example, you’re evaluating the quality of code generation. You might check: does the code compile? Do unit tests pass? Are formatting standards followed?

Or you’re evaluating chatbot responses. You might check: does the response contain prohibited words? Is it too short or too long? Does it contain required elements (e.g., a link to documentation)?

Such LLM fine-tuning evaluation metrics are fully reproducible and inexpensive to calculate. However, they only cover formal aspects, not semantics and meaning.

Metric categories and examples

Statistical metrics

To implement the benchmark approach described above, we need formulas to calculate the score. The oldest and simplest metrics work at the level of word or phrase matching.

BLEU (Bilingual Evaluation Understudy) was originally created to evaluate machine translation. It counts how many n-grams (sequences of 1, 2, 3, or 4 words) from the model’s response appear in the reference response. The more matches, the higher the score (from 0 to 1).

The problem with BLEU is that it doesn’t understand the meaning. If the model outputs “auto halted” and the reference output is “car stopped,” there is no match, even though the meaning is the same. BLEU will give a low score, even though the translation is correct.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) works similarly, but focuses on recall rather than precision. It checks how much information from the reference output made it into the model’s response. It is often used to evaluate summarization.

METEOR attempts to solve BLEU’s problems by taking into account synonyms and word forms. It considers “car” and “automobile” to be similar. But this is still a superficial comparison without a deep understanding of meaning.

All these metrics are fast, cheap, and reproducible. However, they correlate poorly with human assessment of quality for complex tasks. They are insufficient for modern generative models.

Machine learning development companies typically use these metrics as a baseline, but supplement them with more advanced approaches.

LLM metrics evaluation can be grouped by what they measure. Here are the main categories that are important for business.

For example, the model must extract all company mentions from a text. It found 8 references, 6 of which were valid (2 were errors). However, there were 10 company mentions in the text.

Precision = 6/8 = 0.75 (75% correct) Recall = 6/10 = 0.6 (60% correct) F1 = 2 * (0.75 * 0.6) / (0.75 + 0.6) = 0.67

F1 balances between not missing correct references and not adding unnecessary ones.

Here’s a simple code for the calculation:

def calculate_f1_entity_extraction(predictions, ground_truth):

    “””

    Calculates F1-score for entity extraction tasks.

    predictions: list of items found by the model (e.g., [‘Apple’, ‘Google’])

    ground_truth: list of expected items (e.g., [‘Apple’, ‘Microsoft’, ‘Google’])

    “””

    # Convert lists to sets to handle unique items and ignore order

    pred_set = set(predictions)

    true_set = set(ground_truth)

    # True Positives: items found by model AND present in ground truth

    true_positives = len(pred_set.intersection(true_set))

    # If no entities were found or expected, handle edge cases

    if len(pred_set) == 0:

        return 0.0

    precision = true_positives / len(pred_set)

    recall = true_positives / len(true_set)

    if precision + recall == 0:

        return 0.0

    f1 = 2 * (precision * recall) / (precision + recall)

    return f1

# Example corresponding to your text

# Text had 10 companies. Model found 8, but 2 were wrong (so 6 correct).

ground_truth = [“Apple”, “Google”, “Microsoft”, “Amazon”, “Tesla”, “Nvidia”, “Intel”, “AMD”, “Meta”, “Netflix”]

predictions = [“Apple”, “Google”, “Microsoft”, “Amazon”, “Tesla”, “Nvidia”, “WrongName1”, “WrongName2”]

score = calculate_f1_entity_extraction(predictions, ground_truth)

print(f“F1-score: {score:.2f}“)

Faithfulness and hallucination metrics

One of the main problems with LLM is that models sometimes invent facts that sound convincing but don’t correspond to reality. This is called hallucinations.

Faithfulness (source fidelity) is the extent to which a model’s response matches the information in the provided documents. This is especially important for RAG systems, where the model must respond strictly based on your data.

It’s checked by taking each statement from the model’s response and checking whether it exists in the source documents or follows logically from them.

Hallucination Rate (frequency of hallucinations) is the percentage of responses that contain fictitious information. This can be measured manually (experts check a sample of responses) or automatically using LLM-as-a-judge.

Answer Relevance (answer relevance) is the extent to which an answer truly answers the question. A model may provide factually correct, but irrelevant information.

Bias, fairness, and toxicity metrics

LLMs can reproduce biases in training data or generate inappropriate content. These are critical risks for customer service, HR, and public applications.

Toxicity Score is the likelihood that a text contains insults, hate speech, or threats. It is typically measured by specialized models such as Google’s Perspective API.

Bias Detection is a test to ensure that the model does not discriminate based on gender, race, age, or nationality. It is tested on pairs of similar queries where only the demographic characteristics vary.

Fairness Metrics is a set of metrics that verify the uniformity of quality across different user groups. The model should perform equally well for everyone, not just a select few.

Advanced assessment techniques

Basic metrics provide a general idea of ​​quality, but production systems require more sophisticated approaches.

For example:

– Response length is within the acceptable range (100-500 words)

– Response contains required elements (salutation, documentation link)

– Response does not contain prohibited phrases or keywords

– Response structure follows a template

Advantages: Full reproducibility, zero cost after coding, instant verification speed. Disadvantages: Doesn’t catch semantic issues, requires time to write rules, and is inflexible when requirements change.

LLM-as-a-Judge: You use a powerful model to evaluate responses. You specify criteria in the prompt, the model analyzes them, and provides a score. Advantages: flexibility, can evaluate complex aspects (tone, style, logic), no rules programming required. Disadvantages: cost (each assessment is an API request), variability in results, risk of bias in the judge model.

In practice, a combination of quick code checks (weeding out obviously bad answers), then LLM-as-a-Judge for the remaining ones (evaluating qualitative aspects) works better.

Composite and hybrid metrics

A single metric doesn’t cover all aspects of quality. An answer may be factually accurate, but too long. Or it may be short and clear, but full of meaning.

A composite metric combines several simple metrics into one with weights. For example:

Final Score = 0.4 × Accuracy + 0.3 × Faithfulness + 0.2 × Relevance + 0.1 × Readability

Weights are selected based on your business priorities. For a medical app, faithfulness would receive a weight of 0.5. For a marketing generator, relevance and creativity may be more important than accuracy.

A hybrid metric combines reference-based and reference-free approaches. For example, BERTScore uses BERT embeddings for semantic comparison with a reference (smarter than simple word matching), but still requires a reference.

Continuous real-time evaluation

Most metrics are calculated post-factum on a test set. However, in production, a model can degrade over time:

The provider updated the model, and its quality changed; users started asking new types of questions; your corporate data was updated, but the RAG is working with outdated information.

Continuous evaluation is real-time monitoring of metrics in production. You log every request and response, periodically (hourly/daily) calculate metrics based on the latest data, and plot trend graphs.

If a metric drops sharply, the system sends an alert. You can roll back to the previous version of the model or prompt until you understand the problem.

For quick evaluation, lightweight metrics (response length, presence of keywords, simple rule-based checks) are used. For detailed analysis, a selective in-depth evaluation is performed using LLM-as-a-Judge or a human.

Different tasks require different metrics. What’s important for a chatbot is irrelevant for a code generator.

Summarization

Task: The model must compress a long text into a summary, preserving the key points.

Critical metrics:

ROUGE – information coverage from the original

Compression Ratio – how compressed the text is (summary length / original length)

Factual Consistency – does the summary contain facts that weren’t in the original

Coverage – does it cover all the key topics of the original text

Specific problem: The model may simply copy sentences from the original. Technically, this will yield a high ROUGE, but it’s not summarization. An abstractiveness metric is needed, which tests the extent to which the summary restates the original in its own words.

Conversational AI

Task: The model maintains a dialogue with the user, maintains context, and responds naturally.

Critical metrics:

Context Retention — does the model remember what was discussed previously in the dialogue?

Response Relevance: does the response match the current context of the conversation?

Engagement — does the response contribute to the continuation of the dialogue? 

Consistency — does the model not contradict itself within a single dialogue?

Turn-taking appropriateness: Does the model switch roles naturally without interrupting or lagging?

For AI development services, the Average Conversation Length metric is also important: how many exchanges occur before the dialogue ends. Depending on the use case, short conversations may indicate either efficient task completion or poor engagement.

Instead of creating your own tests from scratch, you can use public benchmarks, standard sets of problems on which researchers and companies test models.

The advantage of public benchmarks: you can compare your model with results from other companies and research labs. Model providers publish their results on these benchmarks.

Disadvantage: these datasets may be in the model’s training data (data contamination). The model will perform well not because it’s smart, but because it saw these tasks during training.

Custom datasets

Public benchmarks may be irrelevant for your specific case. If you’re building a medical chatbot in Ukrainian, MMLU (in English, general knowledge) won’t be of much use.

It’s better to create your own dataset:

The training portion can be used to improve prompts and fine-tune the model. Validation is for interim verification. The test is for final evaluation, which you never use during development (to avoid overtraining on tests).

A custom dataset takes time and expertise to create, but it’s the only way to get a relevant evaluation for your specific needs.

Best practices for LLM evaluation

A combination of human and automated assessment

Automated metrics are fast and inexpensive, but not always accurate. Human assessment is accurate, but expensive and slow.

Optimal strategy:

– Automated metrics for all responses in production (continuous monitoring)

– LLM judge for sampling (e.g., 10% of random responses daily)

– Human assessment for critical cases and calibration of automated metrics

Human assessment is not needed to check every response, but for calibration of automated metrics (check whether they correlate with human assessments); analysis of complex cases where automation fails; periodic testing (e.g., 100 random responses once a month)

Working with an LLM development company typically involves customizing this multi-tiered assessment pipeline tailored to the specifics of your business.

Common mistakes and how to avoid them

Mistake 1: Focusing on a single metric. A model can have high accuracy, but still be delusional or toxic. Solution: Use a set of metrics that cover different aspects.

Mistake 2: Overfitting to a test dataset. You test changes on the test set, tweaking prompts until the metric increases. Result: high test results, poor quality in production. Solution: Keep the test set isolated, use validation for experiments.

Mistake 3: Ignoring edge cases. A model performs well on common questions, but fails on rare or complex ones. Solution: Create a test dataset specifically with edge cases.

Mistake 4: Lack of a baseline. You evaluate the model, but have nothing to compare it to—good or bad. Solution: First, measure the current process (how people work without AI); this is your baseline. Then compare the AI ​​to this baseline.

Mistake 5: Underestimating the human factor. Automatic metrics show growth, but users are dissatisfied. Solution: regularly collect user feedback (thumbs up/down, text comments, NPS).

Working with an experienced analytics services provider helps you build a metrics system that truly reflects business value, not just pretty numbers.

A one-time evaluation during development is fine. But for production, an automated, continuous pipeline is needed.

Typical architecture:

1. Data collection. Each request to the model is logged: user question, model response, context (if RAG), metadata (time, user ID, model version).

2. Quick checks. Lightweight rule-based checks are run immediately after the response is generated: length, presence of prohibited words, basic structural requirements. If the check fails, the response is not shown to the user and is logged as an error.

3. Deferred evaluation. Once an hour or day, the system takes the last N responses and runs them through more complex metrics: an LLM-as-a-judge, faithfulness check, and toxicity analysis. The results are aggregated and stored.

4. Monitoring and alerts. The dashboarding system (Grafana, Datadog, custom) displays metric graphs in real time. If a metric exceeds the specified limits, an alert is sent to Slack or email. 

5. Human validation. Periodically (for example, every Friday), the system selects a random sample of responses for human review. The results are used to calibrate automated metrics.

6. A/B testing. When implementing changes (new model, new prompt), some users receive the old version, others the new one. After a week, metrics and user reactions are compared. If the new version is better, everyone rolls out.

This pipeline requires investment in infrastructure, but pays off quickly. You see problems before they become incidents. You can make informed decisions about improvements. You have data for reporting to stakeholders.

The future of LLM evaluation

Evaluation technologies are evolving alongside the models themselves. What’s next?

Currently, when a model makes an error, it’s often unclear why. Tools are emerging that analyze the reasoning behind models and show at what step the error occurred. This helps not only identify the problem but also understand how to fix it.

Instead of a fixed set of LLM performance evaluation metrics, the system will automatically determine which aspects are important for a specific request. For a factual question, the priority will be accuracy; for a creative question, originality and engagement.

Models will begin to automatically assess the confidence in their answers. Instead of providing any answer, the model will say, “I’m 95% sure” or “I’m not sure, it’s better to consult a human.” This will reduce the risks of automation.

Key metrics for ROI of LLM evaluation platform will take into account not only the quality of an individual answer but also the overall experience: response speed, how many clarifications were required, whether the answer solved the user’s problem, and whether the user returned.