9 Accuracy Metrics to Evaluate AI Model Performance

Your LLM deployment is live and generating responses. Users interact with it daily. But without the right performance metrics, you don't know whether it's actually delivering the experience you designed. 

Response times might be creeping up. Token costs could be higher than necessary. Quality issues might be emerging in specific use cases. 

Most teams start tracking metrics after problems surface. The more innovative approach is understanding which measurements matter before deployment. That way, you can catch issues early and optimize what actually impacts users. 

Accuracy Metric #1: Precision

Precision measures what percentage of your positive predictions are actually correct: 

Precision = True Positives / (True Positives + False Positives)

When your model flags something as positive, precision tells you how often it's right.

Let’s say your fraud detection model flags a transaction as suspicious and freezes the card. The transaction was legitimate. When this pattern repeats across hundreds of transactions, you need to know how often your model is wrong when it says "yes."

Your spam filter flags 100 emails, and 90 are actually spam, precision of 0.90. The remaining 10 legitimate emails end up in spam, including that important client message your team needed to see..

Different applications tolerate different precision levels based on what false positives cost:

• Email spam filters need 0.95+ because users abandon systems that hide important emails

• Fraud detection systems target 0.90+ since false declines create support calls and lost sales

• Content moderation requires 0.85+ to avoid removing legitimate content

• Medical screening tools aim for 0.92+ to minimize unnecessary procedures

Low precision means wasted effort. Your review team checks 100 flagged items. 40 are false alarms. That's time not spent investigating actual issues. Applications that block legitimate actions and damage trust need tight control over false positives.

Precision is the right metric when specific conditions apply:

• Manual review of flagged items is expensive, and you need high confidence in predictions

• Blocking legitimate user actions creates friction or damages trust

• False positives generate support volume or operational costs

• The cost of investigating false alarms outweighs occasionally missing a real case

Precision has a blind spot. Your model flags 10 transactions all year and gets all 10 right, perfect precision. Looks great on the dashboard. But it missed 1,000 actual fraud cases because it was too conservative. Precision only tracks what you flagged, not what you missed. That's where recall comes in.

If tracking precision across customer segments is important for your use case, Galileo breaks it down by type in your dashboard. That way you see which groups need different thresholds and can adjust them separately.

Accuracy Metric #2: Recall

Recall measures what percentage of actual positive cases your model successfully identifies: 

Recall = True Positives / (True Positives + False Negatives). 

When real positive cases exist in your data, recall tells you how many your model catches.

Your medical screening model reviews 1,000 patient scans. It correctly identifies 85 of the 100 patients who actually have the condition. The other 15 patients get cleared as healthy and go untreated. Those missed cases mean delayed diagnosis and worse outcomes.

Your model catches 85 out of 100 actual disease cases—recall of 0.85. You missed 15 cases that needed attention.

Different applications set different recall targets based on what missing cases costs:

• Medical screening targets 0.98+ because missing a disease diagnosis can be life-threatening

• Security threat detection aims for 0.95+ as one missed breach can compromise entire systems

• Predictive maintenance requires 0.90+ to catch equipment failures before they cause downtime

• Fraud detection balances around 0.85+ based on whether missed fraud or false positives cost more

Recall is the right metric when missing cases creates bigger problems than false alarms:

• False negatives carry significant risk (missed diseases, security breaches, equipment failures)

• The cost of missing a real case outweighs investigating extra false positives

• Comprehensive coverage matters more than precision in optimistic predictions

• You're willing to accept higher false alarm rates to ensure critical cases get flagged

High recall comes with a trade-off. To catch 95 out of 100 real security threats, your system might flag 200 false alarms daily. Your security team investigates all 200 to avoid missing the one real attack.

Push recall too high and your model flags everything as positive. Perfect recall, but your team drowns in false positives. Recall doesn't track this problem; you need both metrics together. But, monitoring recall across transaction types with each model update requires re-evaluating detection rates for every category after deployment. 

Galileo monitors recall by transaction type in production, showing you which segments underperform so you can address weak coverage before missing critical fraud cases.

Ready to level up your metrics thinking? Learn how to make smarter evaluation decision, and how to build metrics intuition instead of just collecting numbers:

Accuracy Metric #3: F1 Score

F1 Score combines precision and recall into one metric. Both precision and recall need to be reasonably high to get a good F1 score; you can't compensate for weak recall with strong precision or vice versa.

F1 Score = 2 × (Precision × Recall) / (Precision + Recall). I

Your fraud detection system tests two models. Model A achieves 0.95 precision but only 0.60 recall, accurate when it flags fraud but misses half the cases. Model B flips this, with 0.60 precision and 0.95 recall—catching most fraud but generating many false alarms. Neither is clearly better.

F1 helps you compare them with a single number. A balanced model with 0.80 precision and 0.80 recall gets F1 of 0.80. Both Model A and Model B score only 0.74 F1 despite their impressive individual metrics. The balanced model wins because F1 penalizes extreme imbalances.

Production systems typically target different F1 ranges based on the application:

• Fraud detection targets 0.80-0.85 because teams need to balance catching fraud against customer friction

• Document classification aims for 0.75+ to ensure proper routing without overwhelming review teams

• Anomaly detection accepts 0.70+ since some alert fatigue is preferable to missing critical issues

• Customer churn prediction seeks 0.75+ to identify at-risk customers without wasting retention budget

F1 works well with imbalanced datasets where accuracy misleads. A fraud dataset with 100 fraudulent transactions out of 10,000 total achieves 99% accuracy by labeling everything legitimate. F1 performs poorly here because you're missing all the fraudulent cases.

F1 treats precision and recall equally. If missing a fraud case costs 10x as much as a false decline, F1 doesn't capture that asymmetry. You might need weighted versions or track precision and recall separately based on actual business costs.

Tracking how F1 changes across model versions reveals whether updates improve overall performance or just shift the precision-recall balance. 

If this visibility matters for your deployment process, Galileo can help you monitor F1 alongside precision and recall, showing whether model changes actually improve performance or just shift errors.

Accuracy Metric #4: AUC-ROC

AUC-ROC (Area Under the Receiver Operating Characteristic Curve) measures how well your classifier separates positive and negative cases across all possible thresholds. 

The ROC curve plots true positive rate against false positive rate at every threshold. AUC summarizes this into a single value between 0 and 1, where 0.5 indicates random guessing and 1.0 indicates perfect separation.

Your credit scoring model assigns risk scores to loan applications. Some applicants score 0.82, others 0.45, others 0.67. Where do you draw the line between "approve" and "reject"? AUC-ROC tells you how well the model separates good borrowers from risky ones, regardless of where you set that cutoff.

A model with AUC of 0.85 means that 85% of the time, it ranks a randomly chosen positive case higher than a randomly chosen negative case. AUC of 0.75 is barely usable, while 0.90+ indicates strong separation.

Production systems set different AUC targets based on the application:

• Credit scoring requires 0.75+ before models go live, with top performers reaching 0.85+

• Churn prediction aims for 0.80+ to identify at-risk customers reliably

• Medical diagnosis targets 0.85+ since poor separation means misdiagnoses

• Fraud detection seeks 0.80+ to catch suspicious patterns while minimizing false flags

AUC-ROC is the right metric when threshold flexibility matters:

• Business requirements change frequently, and you need to adjust cutoffs without retraining

• You're working with imbalanced datasets where overall accuracy misleads

• You need to evaluate model quality independently of threshold selection

• Comparing different models before deciding on operational thresholds

AUC-ROC works well with imbalanced datasets and changing business requirements. You can evaluate the model once, then adjust your threshold later without retraining when your tolerance for false positives changes.

AUC doesn't tell you which threshold actually to use. A model with an AUC of 0.90 could still perform poorly if you pick the wrong cutoff for your business needs. It also doesn't reveal whether the predicted probabilities are well-calibrated; a model might perfectly separate the classes but still give misleading confidence scores.

Finding the optimal cutoff manually means running experiments for each scenario. Galileo visualizes ROC curves alongside precision-recall tradeoffs from your production data. 

That way, you can adjust thresholds based on actual business costs without running separate threshold experiments for every requirement change.

Accuracy Metric #5: Mean Absolute Error (MAE)

Mean Absolute Error measures the average distance between your predictions and actual values: MAE = (1/n) × Σ|Actual - Predicted|. It's expressed in the same units as your target variable, making it easy to interpret.

Your demand forecasting model predicts you'll need 1,200 units next week. You actually need 1,150. That's an error of 50 units. MAE averages these errors across all predictions to show typical deviation.

Your model predicts daily sales of [120, 135, 150] units, while the actual values are [100, 140, 160]. The absolute errors are 20, 5, and 10. MAE is 11.7 units, your model misses by about 12 units on average.

Production systems use MAE when prediction errors have linear cost:

• Inventory forecasting targets MAE under 10% of average demand to avoid stockouts or excess

• Energy load prediction aims for MAE within 5% of the peak load to maintain grid stability

• Sales forecasting seeks MAE below 15% to support resource planning

• Temperature control systems need MAE under 2 degrees to maintain comfort

MAE treats all errors equally. Missing by 100 units gets the same weight as missing by 10 units. This works when small and large errors cost roughly the same. If big misses are catastrophic—like running out of critical inventory, you might need RMSE instead.

MAE works best in specific scenarios where error patterns matter:

• Cost of error scales linearly (missing by 20 is twice as bad as missing by 10)

• You care about typical performance, not worst-case scenarios

• Outliers shouldn't dominate your evaluation

• Stakeholders need results in original units they understand

Overall MAE looks acceptable across your forecasts, but that aggregate number hides problems in specific segments. Your electronics category might be twice as inaccurate as clothing, or certain regions could be consistently off. 

Tracking MAE across different product lines, regions, and time periods manually means pulling data, segmenting it, and recalculating—work that eats up time you'd rather spend fixing the actual forecasting issues. 

Overall, MAE appears acceptable across your forecasts, but the aggregate number masks problems in specific segments. Your electronics category might be twice as inaccurate as your clothing category, or specific regions might be consistently off. 

Accuracy Metric #6: Root Mean Squared Error (RMSE)

Root Mean Squared Error measures prediction accuracy by squaring errors before averaging them: 

RMSE = √[(1/n) × Σ(Actual - Predicted)²]. 

The squaring amplifies large errors, making RMSE more sensitive to outliers than MAE.

Your inventory model predicts daily demand of [100, 100, 100] units against actual values of [98, 102, 95]. MAE is 3.0 units—treating all errors equally. RMSE is 3.74 units—penalizing that five-unit miss more heavily.

The difference matters when large errors cost disproportionately more. Running out of critical inventory by 50 units causes more than 5x the damage of being short by 10 units. RMSE captures this asymmetry.

Production systems target RMSE based on how outliers impact operations:

• Trading algorithms need RMSE within 1-2% of price ranges since large misses trigger significant losses

• Energy load forecasting targets RMSE under 3% of peak load to prevent grid instability

• Supply chain planning seeks RMSE below 10% to avoid stockout cascades

• Financial modeling requires RMSE under 5% as large prediction errors compound

RMSE is the right metric when large errors create outsized problems:

• Cost increases nonlinearly with error size (being wrong by 100 is more than 10x worse than being wrong by 10)

• Outliers and worst-case scenarios deserve extra attention

• A few large misses can wipe out gains from many accurate predictions

• You need to penalize models that occasionally produce terrible predictions

Research shows healthy models maintain RMSE around 1.5x their MAE. If your RMSE is significantly higher, you have fat-tailed errors that deserve investigation. A few terrible predictions might be dragging down overall performance.

RMSE is less interpretable than MAE because squaring changes the units. RMSE of 15 doesn't mean your average error is 15—it means something more abstract about the distribution of your errors.

Your RMSE looks reasonable overall, but one product line might occasionally experience massive forecast misses, which could inflate the number. 

Examining error distributions across segments shows where outlier predictions concentrate. Identifying these patterns helps you understand whether extreme errors come from data quality issues, seasonal anomalies, or fundamental model limitations in specific categories.

Accuracy Metric #7: BLEU Score

The BLEU Score (Bilingual Evaluation Understudy) evaluates machine translation by comparing generated text to reference translations through n-gram overlap. 

It calculates precision for 1-gram through 4-gram matches, takes the geometric mean, then applies a brevity penalty to discourage overly short outputs. Scores range from 0 to 100, with higher values indicating better quality.

Your translation system converts "The meeting starts at 3pm" to French. The reference is "La réunion commence à 15 h." BLEU checks how many 1-word, 2-word, 3-word, and 4-word sequences match between your output and the reference.

A commercial English-to-French system might score 38 BLEU. That means roughly 38% n-gram overlap with human references, enough for comprehensible translation but showing room for improvement.

Production systems set different BLEU targets based on language pair and domain:

• High-resource language pairs (English-French, English-Spanish) target 35-40 for commercial quality

• Technical documentation translation aims for 30-35 since specialized terminology is harder

• Marketing content seeks 40+ because fluency and natural phrasing matter more

• Low-resource language pairs accept 25-30 due to limited training data

BLEU is the right metric when surface-level accuracy matters:

• You need fast, reproducible evaluation without human reviewers

• Exact terminology and phrasing are critical (legal, technical docs)

• You're comparing multiple translation systems on the same content

• Development speed matters more than capturing semantic nuances

BLEU favors literal translations over paraphrases. Your model might produce "La réunion débute à 15h" (starts vs. begins)—semantically identical but scored lower because "débute" doesn't match "commence." This blindness to meaning pushes many teams toward BERTScore.

BLEU also ignores word order within matched n-grams. A grammatically broken sentence can score well if the right words appear in roughly the right places. Your translation quality varies dramatically across document types, yet BLEU masks this. Legal documents might score 35 while marketing content hits 45. 

Tracking how these scores change with model updates means re-running evaluations across every content category after each deployment. 

Galileo continuously monitors BLEU by document type in production, alerting you when specific categories degrade so you catch translation quality issues before users notice them.

Accuracy Metric #8: ROUGE

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures how much of the reference summary appears in your generated summary. Unlike BLEU's precision focus, ROUGE emphasizes recall, and ensures your summary captures key information from the source.

Your model summarizes a 500-word article into 50 words. The reference summary contains 60 words. ROUGE-1 counts how many individual words from the reference appear in your summary. ROUGE-L looks for the longest standard sequence to reward preserving narrative flow.

A news summarization system might achieve ROUGE-1 of 0.45 and ROUGE-L of 0.42. That means 45% of reference words appear in the summary, with strong sequence preservation.

Production systems target different ROUGE scores based on summarization task:

• News summarization aims for ROUGE-L above 0.40 to capture key facts

• Meeting notes target ROUGE-1 of 0.50+ since action items must be preserved

• Research paper abstracts seek ROUGE-2 of 0.25+ to maintain technical accuracy

• Legal document summaries require ROUGE-L of 0.45+, as missing key terms create liability

ROUGE is the right metric when completeness matters more than brevity:

• Missing critical information from the source is worse than including extra details

• You're summarizing content where recall of key facts is essential

• The source contains specific terms or names that must appear in summaries

• You need to ensure summaries don't omit important context

ROUGE-1 counts individual word overlap, easy to game by copying words without preserving meaning. ROUGE-2 analyzes 2-word sequences to improve signal quality. ROUGE-L measures the longest common subsequence, rewarding summaries that maintain source structure.

The metric still misses paraphrases. Your model might write "earnings declined" when the reference says "profits dropped", semantically identical but scored as different. This surface-level matching is why many teams combine ROUGE with semantic metrics.

Summary quality differs across content length and domain, but aggregate ROUGE masks the variation. Long-form articles might perform well while technical docs struggle. Tracking how these patterns change across model versions means re-evaluating thousands of summaries every time you deploy updates. 

Galileo automatically monitors ROUGE across content segments in production, helping you spot where quality drops and whether your latest model actually improves the categories that matter.

Accuracy Metric #9: BERTScore

BERTScore evaluates text generation by comparing contextual embeddings instead of counting surface-level word matches. It uses transformer models to embed each token in both generated and reference texts, matches tokens using cosine similarity, and averages these similarities to compute precision, recall, and F1 scores.

Your model generates "The cat sits on the mat" while the reference is "A cat is on the mat." BLEU penalizes the synonym substitution and scores 0.54. BERTScore recognizes semantic equivalence and scores 0.92.

State-of-the-art translation systems reach BERTScore around 0.90, correlating far better with human judgments than BLEU's 35-40 range on the same outputs.

Production systems set different BERTScore targets based on how much semantic accuracy matters:

• Creative content generation aims for 0.88+ since meaning matters more than exact wording

• Conversational AI targets 0.85+ to ensure responses stay semantically appropriate

• Question answering seeks 0.90+ because wrong meanings completely fail the task

• Content rewriting requires 0.92+ to preserve original intent across style changes

BERTScore is the right metric when semantic accuracy outweighs surface similarity:

• Paraphrases and synonyms should be rewarded, not penalized

• The task involves creative generation where exact wording varies

• You're evaluating conversational systems where meaning trumps exact phrases

• Traditional metrics show poor correlation with human quality judgments

BERTScore requires running transformer inference for every evaluation, making it computationally expensive. Batch-scoring thousands of outputs needs GPU memory and processing time that BLEU avoids.

The metric's sophistication also means it's harder to debug. When BERTScore drops, you can't easily identify which specific words or phrases caused the decline, like you can with n-gram overlap.

Your model's BERTScore varies across subject domains, but the aggregate number hides this. Medical content might score 0.88 while financial content hits 0.82. You ship a model update. Did it improve weak domains without hurting strong ones?

Checking means re-evaluating semantic quality across every category after deployment. Galileo tracks BERTScore trends by domain as models evolve, revealing which content types improve or degrade with each version you deploy.

Track AI Model Performance with Galileo

Galileo automates the monitoring of these accuracy metrics across segments and model versions in production, catching performance issues such as precision drops in specific customer groups before they affect users.

Here’s how Galileo wraps evaluation, tracing, and guardrailing into a single cohesive workflow:

• Automated quality guardrails in CI/CD: Galileo integrates directly into your development workflow, running comprehensive evaluations on every code change and blocking releases that fail quality thresholds

• Multi-dimensional response evaluation: With Galileo's Luna-2 Small Language  models, you can assess every output across dozens of quality dimensions—correctness, toxicity, bias, adherence—at 97% lower cost than traditional LLM-based evaluation approaches

• Real-time runtime protection: Galileo's Agent Protect scans every prompt and response in production, blocking harmful outputs before they reach users while maintaining detailed compliance logs for audit requirements

• Intelligent failure detection: Galileo’s Insights Engine automatically clusters similar failures, surfaces root-cause patterns, and recommends fixes, reducing debugging time while building institutional knowledge

• Human-in-the-loop optimization: Galileo's Continuous Learning via Human Feedback (CLHF) transforms expert reviews into reusable evaluators, accelerating iteration while maintaining quality standards

Get started with Galileo today and discover how comprehensive observability can elevate your agents' development and achieve reliable AI agents that users trust.