AI model performance metrics: in-depth guide

Your AI model is deployed to production. But how do you know if it is performing as expected? Many organizations use human feedback loops to assess output quality. However, manual checks are limited by human judgment and are less useful at scale. Enterprise-grade quality control requires AIOps implementation. Your goal should be to set up a CI/CD/CT pipeline that continuously integrates, tests, and deploys model changes in production.

But how are you going to quantify any improvements achieved? That’s where metrics come into play. AI model performance metrics present a system of evaluation and comparison. You evaluate the model in exactly the same manner, using the same dataset, both before and after a change. Comparing the two values gives you a clear measure of improvement.

This article explores different AI model metrics and their evaluation methods. Like many open-source AI models you can work with, there are innumerable metrics and benchmarks, and new papers on different metrics are published frequently. We only cover popular examples in this guide.

Model quality metricsModel quality metrics

These metrics determine whether your AI model output addresses the given input in a concise and informative manner.

You can broadly group them into two categories:

1. Statistical scorers statistically compare model output with a predefined ideal output (reference text).

2. Model-based scorers use one AI model to evaluate the output of another.

We give some examples below.

PerplexityPerplexity

Perplexity measures how well a language model predicts a word sequence. It relates to the next word’s probability distribution in your reference text sequence. A model assigning high probabilities to the correct next word receives a low perplexity score, indicating accuracy.

BLEU scoreBLEU score

The Bilingual Evaluation Understudy (BLEU) score compares the n-grams (n consecutive words) with those of a reference text for translation tasks. A higher BLEU score indicates closer alignment between the generated text and the reference.

ROUGE scoreROUGE score

Recall-Oriented Understudy for Gisting Evaluation (ROUGE) evaluates how many n-grams from the reference text are captured in the generated output. It measures the output quality of summary tasks by highlighting how much critical information is retained in the generated summary.

Word error rate (WER)Word error rate (WER)

Word Error Rate (WER) measures the number of substitutions, deletions, and insertions needed to transform the generated output into the reference text. It is useful for measuring the output quality of speech-to-text tasks. A lower WER indicates better output quality.

METEORMETEOR

Metrics like BLEU, WER, etc., check how closely your AI model output matches the reference text. They are not useful for evaluating open-ended tasks as they penalize phrasing variations (even if the output is otherwise semantically correct).

The Metric for Evaluation of Translation with Explicit ORdering (METEOR) addresses some weaknesses. It uses stemming, synonym matching, and ordering constraints to evaluate model output more flexibly. It considers the semantic meaning of words rather than n-gram matches.

BERTScoreBERTScore

Some metrics, like BERTScore and BLEURTScore, use the LLM BERT to compare your model’s output with reference text. Instead of comparing text with text, BERT compares the vector embeddings. It converts reference and output text to their mathematical representation and compares them at that level. It can factor in semantics and assess more complex models.

GPTScoreGPTScore

GPT compares your model’s output with the one it generates. For example, let’s say your model generates a document summary. GPT also generates the same results and then compares them. It assesses your model’s output against self-generated text to calculate the GPTScore.

RAG metricsRAG metrics

Many organizations implement RAG workflows as part of their AI model deployment. It is a cost-effective way of giving pre-trained AI models access to new information that is not part of their training data.

User input to the AI model is first passed to an intelligent search system. The system retrieves data relevant to the user’s query from the organization’s database and then passes it to the model. The model’s final output combines information from its training data and the organization’s knowledge base.

If your team implements AI models with RAG workflows, consider including the following metrics.

FaithfulnessFaithfulness

This metric checks how accurately your model’s output reflects the RAG workflow information. For example, let’s say your RAG workflows retrieve HR policy documents for your model’s reference. If your model quotes HR policies from the document, it gets a high faithfulness score.

Other metricsOther metrics

Other metrics mathematically check your model’s final output against retrieved data.

For example:

User metricsUser metrics

Even if your AI model’s output is high quality, you need to check if it meets the needs of your end users. For example, if your users want text summaries but your AI model is better at classification, the output is not what the users expect. You must check user engagement with your model to justify costs, increase revenue, and justify any new feature changes. Some examples of metrics in this category.

User satisfactionUser satisfaction

You calculate it manually from survey data or ratings that users provide. For quick feedback, you can simply ask users to rate the AI model response with some stars, thumbs up, or thumbs down. For more detailed information, ask pointed questions that can be mathematically aligned.

For example:

High values indicate alignment between model performance and user expectations.

Engagement rateEngagement rate

Measures how often users interact with the AI model’s output. You can calculate it by computing the number of daily/weekly/monthly:

• Users who submit prompts

• AI model responses without errors

• User views of responses

• User clicks on any reference links in the response

Retention rateRetention rate

Monitors how often users return to use the AI model over time. High retention suggests users find continuous value in AI outputs, while low retention could indicate a lack of perceived usefulness.

Conversion rateConversion rate

This metric assesses how often interactions with the AI model lead to desired outcomes, such as purchases or other business goals. It is useful for mapping investment vs. revenue growth and returns for commercial models.

Speed metricsSpeed metrics

Speed metrics track model efficiency. You want the AI model to generate timely responses with minimum resource consumption. Some examples include…