{"id":15664,"date":"2026-11-09T14:06:45","date_gmt":"2026-11-09T08:36:45","guid":{"rendered":"https:\/\/www.xicom.biz\/blog\/?p=15664"},"modified":"2026-11-09T17:25:47","modified_gmt":"2026-11-09T11:55:47","slug":"how-to-evaluate-ai-models","status":"publish","type":"post","link":"https:\/\/www.xicom.biz\/blog\/how-to-evaluate-ai-models\/","title":{"rendered":"How to Evaluate AI Models: Metrics, Methods &amp; Best Practices"},"content":{"rendered":"\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-background wp-block-paragraph\" style=\"background-color:#f5f5f5\"><em><strong>Quick Answer<\/strong><br><\/em>To evaluate an AI model, define what success means for the business, test the model on held-out data that reflects real usage, and measure task-specific metrics such as precision and recall for classification or faithfulness for LLMs. Then check robustness, fairness, latency, and cost, and keep monitoring the model in production. No single metric is enough. Reliable evaluation combines automated metrics, human review, and live monitoring.<\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">An AI model that scores well in a notebook can still fail in production. It can misclassify the cases that matter most, invent facts with full confidence, slow down under real traffic, or slowly lose accuracy as user behavior changes. AI model evaluation is how you catch these problems before your users do.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This guide explains how to evaluate AI models from start to finish: the metrics that matter for each type of model, the methods teams use to test them, how <a href=\"https:\/\/www.xicom.biz\/llm-development-services\/\">LLM<\/a> evaluation differs from traditional machine learning, and the best practices that keep a model reliable after launch.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Key_Takeaways\"><\/span>Key Takeaways:<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul style=\"background-color:#f5f5f5\" class=\"wp-block-list has-background\">\n<li>Start with business goals, then pick metrics that reflect them. Accuracy alone is often misleading.<\/li>\n\n\n\n<li>Test on data the model has never seen and that looks like real production traffic.<\/li>\n\n\n\n<li>LLMs and AI agents need different metrics, such as faithfulness, hallucination rate, and task completion, plus human or LLM-as-a-judge review.<\/li>\n\n\n\n<li>Latency, cost per request, fairness, and robustness belong in every evaluation, not only quality scores.<\/li>\n\n\n\n<li>Evaluation does not end at launch. Production monitoring catches drift and silent failures.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_Is_AI_Model_Evaluation\"><\/span>What Is AI Model Evaluation?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">AI model evaluation is the process of measuring how well a model performs its intended task, how reliably it behaves across different conditions, and whether it is safe, fair, and cost-effective enough to use in a real product. It covers the full life of the model, from comparing candidate models during development to monitoring the chosen model after deployment.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Good evaluation matters for four reasons:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Quality:<\/strong> it shows whether the model actually solves the problem, not just whether it looks impressive in a demo.<\/li>\n\n\n\n<li><strong>Risk:<\/strong> it exposes errors, bias, and unsafe outputs before they reach customers.<\/li>\n\n\n\n<li><strong>Cost:<\/strong> it helps you choose the smallest, cheapest model that meets your quality bar.<\/li>\n\n\n\n<li><strong>Compliance:<\/strong> regulations such as the EU AI Act require high-risk AI systems to achieve appropriate levels of accuracy and robustness, and documented evaluation is how you show it.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_to_Evaluate_AI_Models_A_Step-by-Step_Process\"><\/span>How to Evaluate AI Models: A Step-by-Step Process<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Whether you are testing a fraud detection model, a computer vision system, or an LLM-powered assistant, the same process applies.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_Define_Success_in_Business_Terms\"><\/span>1. Define Success in Business Terms<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Start with the outcome the model should improve, such as fewer fraud losses, faster ticket resolution, or fewer manual reviews. Then decide which errors cost more. In medical screening, a missed case is far worse than a false alarm. In spam filtering, blocking a real email may be worse than letting some spam through. These trade-offs decide which metrics you prioritize.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_Build_a_Representative_Evaluation_Dataset\"><\/span>2. Build a Representative Evaluation Dataset<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Create a test set, often called a golden dataset, that the model never sees during training. It should reflect real inputs, including edge cases, rare classes, different user groups, and messy data. Check for data leakage, where test examples or near-duplicates slip into training data and make results look better than they are.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_Choose_Metrics_That_Match_the_Task\"><\/span>3. Choose Metrics That Match the Task<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Pick a primary metric that reflects the business goal and a few supporting metrics that guard against blind spots. A classification model might use recall as its primary metric, with precision and latency as guardrails. The next section lists metrics by task type.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_Set_a_Baseline\"><\/span>4. Set a Baseline<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Compare the model against something simple: the current rule-based system, a basic statistical model, or the existing human process. A model is only worth deploying if it beats the baseline by a margin that justifies its cost and complexity.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_Run_Offline_Evaluation\"><\/span>5. Run Offline Evaluation<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Score the model on the test set and break results down by segment, such as region, customer type, language, or device. Strong average performance can hide poor results for an important group.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"6_Test_Robustness_Fairness_and_Safety\"><\/span>6. Test Robustness, Fairness, and Safety<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Check how the model handles noisy, unusual, or adversarial inputs. Compare error rates across demographic groups where that is relevant. For generative AI, test for harmful content, data leakage, and prompt injection.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"7_Measure_Operational_Performance\"><\/span>7. Measure Operational Performance<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Measure latency, throughput, memory use, and cost per request under realistic load. A highly accurate model that is too slow or too expensive for your traffic is not production-ready.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"8_Validate_With_Real_Users\"><\/span>8. Validate With Real Users<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Before full rollout, run the model in shadow mode alongside the current system or launch an A\/B test with a small share of traffic. This shows whether offline gains translate into better business results.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"9_Monitor_Continuously_in_Production\"><\/span>9. Monitor Continuously in Production<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Track quality, drift, latency, and cost after launch, and set alerts for meaningful changes. Re-run your evaluation suite whenever you retrain the model, change a prompt, or switch model versions.<\/p>\n\n\n<section class=\"inquireBlock text-center mt-3\">\n<div class=\"capTxt new\">Not sure your AI model is ready for production?<\/div>\n<div class=\"smallTxt new mt-0 mb-3\">Our AI engineers design evaluation frameworks that test quality, safety, speed, and cost before you launch.<\/div>\n<div class=\"contact-bttn\"><a href=\"https:\/\/www.xicom.biz\/contact\/\">Talk to Our Experts<\/a><\/div>\n<\/section>\n\n\n<p class=\"wp-block-paragraph\"><strong><em>Also Read: <a href=\"https:\/\/www.xicom.biz\/blog\/ai-app-development-cost\/\">AI App Development Cost<\/a><\/em><\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Key_AI_Model_Evaluation_Metrics_by_Task_Type\"><\/span>Key AI Model Evaluation Metrics by Task Type<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-metrics-wheel.webp\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"768\" src=\"https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-metrics-wheel-1024x768.webp\" alt=\"AI Model Evaluation\" class=\"wp-image-15665\" srcset=\"https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-metrics-wheel-1024x768.webp 1024w, https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-metrics-wheel-300x225.webp 300w, https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-metrics-wheel-768x576.webp 768w, https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-metrics-wheel-150x113.webp 150w, https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-metrics-wheel.webp 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The right metrics depend on what the model does. Here are the most widely used AI model evaluation metrics, grouped by task.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Classification_Metrics\"><\/span>Classification Metrics<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Classification models assign inputs to categories, such as fraud or not fraud, or positive, neutral, or negative sentiment.<\/p>\n\n\n\n<figure class=\"wp-block-table aligncenter\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-center\" data-align=\"center\">Metric<\/th><th class=\"has-text-align-center\" data-align=\"center\">What It Measures<\/th><th class=\"has-text-align-center\" data-align=\"center\">When to Use It<\/th><\/tr><\/thead><tbody><tr><td class=\"has-text-align-center\" data-align=\"center\">Accuracy<\/td><td class=\"has-text-align-center\" data-align=\"center\">Share of all predictions that are correct<\/td><td class=\"has-text-align-center\" data-align=\"center\">Balanced classes with similar error costs<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Precision<\/td><td class=\"has-text-align-center\" data-align=\"center\">Of the items flagged positive, how many really are positive<\/td><td class=\"has-text-align-center\" data-align=\"center\">When false alarms are costly, such as spam filters<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Recall<\/td><td class=\"has-text-align-center\" data-align=\"center\">Of all real positives, how many the model found<\/td><td class=\"has-text-align-center\" data-align=\"center\">When missed cases are costly, such as fraud or disease detection<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">F1 score<\/td><td class=\"has-text-align-center\" data-align=\"center\">Harmonic mean of precision and recall<\/td><td class=\"has-text-align-center\" data-align=\"center\">Imbalanced data where both error types matter<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">AUC-ROC<\/td><td class=\"has-text-align-center\" data-align=\"center\">How well the model separates classes across all thresholds<\/td><td class=\"has-text-align-center\" data-align=\"center\">Comparing models before choosing a threshold<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">PR-AUC<\/td><td class=\"has-text-align-center\" data-align=\"center\">Precision and recall trade-off across thresholds<\/td><td class=\"has-text-align-center\" data-align=\"center\">Highly imbalanced data with a rare positive class<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Log loss<\/td><td class=\"has-text-align-center\" data-align=\"center\">How confident and correct the predicted probabilities are<\/td><td class=\"has-text-align-center\" data-align=\"center\">When probability scores drive decisions<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The core formulas are simple. With TP as true positives, FP as false positives, and FN as false negatives:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Precision<\/strong> = TP \/ (TP + FP)<\/li>\n\n\n\n<li><strong>Recall<\/strong> = TP \/ (TP + FN)<\/li>\n\n\n\n<li><strong>F1 score<\/strong> = 2 \u00d7 (Precision \u00d7 Recall) \/ (Precision + Recall)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Always review the confusion matrix too. It shows exactly which classes the model mixes up, which a single score cannot.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Regression_Metrics\"><\/span>Regression Metrics<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Regression models predict numbers, such as demand, prices, or delivery times.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>MAE (mean absolute error):<\/strong> the average size of errors, easy to explain in business units.<\/li>\n\n\n\n<li><strong>RMSE (root mean squared error):<\/strong> penalizes large errors more heavily, useful when big misses are especially costly.<\/li>\n\n\n\n<li><strong>MAPE (mean absolute percentage error):<\/strong> error as a percentage, handy for forecasting, but unreliable when actual values are near zero.<\/li>\n\n\n\n<li><strong>R\u00b2 (coefficient of determination):<\/strong> how much of the variation in the target the model explains.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Computer_Vision_Metrics\"><\/span>Computer Vision Metrics<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">For <a href=\"https:\/\/www.xicom.biz\/computer-vision-development-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">computer vision<\/a> tasks, metrics measure both what the model sees and where it sees it:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>IoU (intersection over union):<\/strong> how closely a predicted bounding box or mask overlaps the true one.<\/li>\n\n\n\n<li><strong>mAP (mean average precision):<\/strong> the standard score for object detection, averaged across classes and IoU thresholds.<\/li>\n\n\n\n<li><strong>Dice coefficient:<\/strong> common in image segmentation, especially medical imaging.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"NLP_and_Text_Generation_Metrics\"><\/span>NLP and Text Generation Metrics<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Traditional text metrics compare model output with reference answers:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>BLEU:<\/strong> word overlap with a reference, mainly used for translation.<\/li>\n\n\n\n<li><strong>ROUGE:<\/strong> overlap focused on recall, mainly used for summarization.<\/li>\n\n\n\n<li><strong>BERTScore:<\/strong> semantic similarity using embeddings, so it credits paraphrases that word-overlap metrics miss.<\/li>\n\n\n\n<li><strong>Perplexity:<\/strong> how well a language model predicts text, useful during training but weakly linked to answer quality.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">These metrics are fast and cheap, but they struggle with open-ended outputs where many different answers can be correct. That is why modern LLM evaluation relies on the metrics below.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"LLM_and_RAG_Evaluation_Metrics\"><\/span>LLM and RAG Evaluation Metrics<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">For chatbots, copilots, and <a href=\"https:\/\/www.xicom.biz\/rag-development-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">RAG systems<\/a>, quality depends on whether answers are correct, grounded, and useful:<\/p>\n\n\n\n<figure class=\"wp-block-table aligncenter\"><table class=\"has-fixed-layout\"><tbody><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>Metric<\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\"><strong>What It Checks<\/strong><\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Faithfulness (groundedness)<\/td><td class=\"has-text-align-center\" data-align=\"center\">Whether every claim in the answer is supported by the retrieved context<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Answer relevance<\/td><td class=\"has-text-align-center\" data-align=\"center\">Whether the answer actually addresses the question asked<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Context precision and recall<\/td><td class=\"has-text-align-center\" data-align=\"center\">Whether retrieval found the right documents and ranked them well<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Hallucination rate<\/td><td class=\"has-text-align-center\" data-align=\"center\">Share of responses that contain unsupported or false statements<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Correctness<\/td><td class=\"has-text-align-center\" data-align=\"center\">Agreement with a verified reference answer<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Instruction following<\/td><td class=\"has-text-align-center\" data-align=\"center\">Whether the output respects format, tone, length, and policy rules<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Toxicity and safety<\/td><td class=\"has-text-align-center\" data-align=\"center\">Whether the output contains harmful, biased, or disallowed content<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Evaluating retrieval and generation separately is important. If an answer is wrong, you need to know whether the retriever missed the right document or the model ignored it.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"AI_Agent_Evaluation_Metrics\"><\/span>AI Agent Evaluation Metrics<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.xicom.biz\/ai-agent-development-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI agents<\/a> take actions across tools and systems, so they are judged on outcomes, not just text:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Task completion rate:<\/strong> how often the agent fully achieves the goal.<\/li>\n\n\n\n<li><strong>Tool-call accuracy:<\/strong> whether it chooses the right tool with the right inputs.<\/li>\n\n\n\n<li><strong>Steps and cost per task:<\/strong> how efficiently it reaches the goal.<\/li>\n\n\n\n<li><strong>Error recovery:<\/strong> how it handles failed tool calls or unexpected results.<\/li>\n\n\n\n<li><strong>Policy compliance:<\/strong> whether it stays within permissions and asks for approval when it should.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Operational_Metrics\"><\/span>Operational Metrics<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Every production model should also be measured on:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Latency:<\/strong> median and p95 response time, plus time to first token for streaming LLMs.<\/li>\n\n\n\n<li><strong>Throughput:<\/strong> requests or tokens handled per second under load.<\/li>\n\n\n\n<li><strong>Cost per request:<\/strong> model, compute, and infrastructure cost for each prediction or conversation.<\/li>\n\n\n\n<li><strong>Reliability:<\/strong> uptime, error rates, and timeouts.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">For self-hosted LLMs, the serving engine has a big effect on these numbers. Our comparison of <a href=\"https:\/\/www.xicom.biz\/blog\/sglang-vs-vllm\/\" target=\"_blank\" rel=\"noreferrer noopener\">SGLang vs vLLM<\/a> covers this in detail.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Fairness_Calibration_and_Robustness_Metrics\"><\/span>Fairness, Calibration, and Robustness Metrics<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Demographic parity and equal opportunity:<\/strong> compare outcomes and error rates across groups to detect bias.<\/li>\n\n\n\n<li><strong>Expected calibration error (ECE):<\/strong> checks whether a model that says it is 80% confident is right about 80% of the time.<\/li>\n\n\n\n<li><strong>Robustness score:<\/strong> the change in performance when inputs contain typos, noise, paraphrases, or adversarial changes.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"AI_Model_Evaluation_Methods\"><\/span>AI Model Evaluation Methods<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-methods.webp\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"556\" src=\"https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-methods-1024x556.webp\" alt=\"How to Evaluate AI Models\" class=\"wp-image-15666\" srcset=\"https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-methods-1024x556.webp 1024w, https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-methods-300x163.webp 300w, https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-methods-768x417.webp 768w, https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-methods-150x81.webp 150w, https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-methods.webp 1182w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Metrics tell you what to measure. Methods tell you how to evaluate AI models in a way you can trust, from early development through live traffic.<\/p>\n\n\n\n<figure class=\"wp-block-table aligncenter\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-center\" data-align=\"center\">Method<\/th><th class=\"has-text-align-center\" data-align=\"center\">How It Works<\/th><th class=\"has-text-align-center\" data-align=\"center\">Best For<\/th><\/tr><\/thead><tbody><tr><td class=\"has-text-align-center\" data-align=\"center\">Holdout split<\/td><td class=\"has-text-align-center\" data-align=\"center\">Separate training, validation, and test sets<\/td><td class=\"has-text-align-center\" data-align=\"center\">Large datasets and quick comparisons<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">K-fold cross-validation<\/td><td class=\"has-text-align-center\" data-align=\"center\">Train and test on several different splits, then average results<\/td><td class=\"has-text-align-center\" data-align=\"center\">Smaller datasets and more stable estimates<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Time-based split<\/td><td class=\"has-text-align-center\" data-align=\"center\">Train on past data and test on later data<\/td><td class=\"has-text-align-center\" data-align=\"center\">Forecasting and any data that changes over time<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Benchmark testing<\/td><td class=\"has-text-align-center\" data-align=\"center\">Score the model on public datasets such as MMLU or HumanEval<\/td><td class=\"has-text-align-center\" data-align=\"center\">Shortlisting foundation models<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Human evaluation<\/td><td class=\"has-text-align-center\" data-align=\"center\">Experts rate outputs using a clear rubric<\/td><td class=\"has-text-align-center\" data-align=\"center\">Open-ended outputs and high-stakes domains<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">LLM-as-a-judge<\/td><td class=\"has-text-align-center\" data-align=\"center\">A strong model scores outputs against a rubric<\/td><td class=\"has-text-align-center\" data-align=\"center\">Scaling LLM evaluation across thousands of samples<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Red teaming<\/td><td class=\"has-text-align-center\" data-align=\"center\">Testers deliberately try to make the model fail or misbehave<\/td><td class=\"has-text-align-center\" data-align=\"center\">Safety, security, and prompt injection testing<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Shadow deployment<\/td><td class=\"has-text-align-center\" data-align=\"center\">The new model runs on live traffic without affecting users<\/td><td class=\"has-text-align-center\" data-align=\"center\">Low-risk validation before rollout<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">A\/B testing<\/td><td class=\"has-text-align-center\" data-align=\"center\">Compare models on business KPIs with real users<\/td><td class=\"has-text-align-center\" data-align=\"center\">Proving business impact<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Production monitoring<\/td><td class=\"has-text-align-center\" data-align=\"center\">Track quality, drift, and cost continuously after launch<\/td><td class=\"has-text-align-center\" data-align=\"center\">Every model in production<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Two of these methods need extra care. Public benchmarks are useful for shortlisting, but models may have seen benchmark questions during training, and a high benchmark score does not guarantee good results on your data. LLM-as-a-judge scales well, but judge models can favor longer answers, answers in a certain position, or outputs from their own model family. Calibrate the judge against a sample of human ratings before relying on it.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong><em>Also Read: <a href=\"https:\/\/www.xicom.biz\/blog\/production-rag-architecture\/\">Production RAG Architecture<\/a><\/em><\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_LLM_Evaluation_Differs_From_Traditional_ML_Evaluation\"><\/span>How LLM Evaluation Differs From Traditional ML Evaluation<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Large language models break several assumptions that classic machine learning evaluation relies on:<\/p>\n\n\n\n<figure class=\"wp-block-table aligncenter\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-center\" data-align=\"center\">Aspect<\/th><th class=\"has-text-align-center\" data-align=\"center\">Traditional ML<\/th><th class=\"has-text-align-center\" data-align=\"center\">LLMs<\/th><\/tr><\/thead><tbody><tr><td class=\"has-text-align-center\" data-align=\"center\">Output<\/td><td class=\"has-text-align-center\" data-align=\"center\">A label or number<\/td><td class=\"has-text-align-center\" data-align=\"center\">Open-ended text, code, or actions<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Correct answer<\/td><td class=\"has-text-align-center\" data-align=\"center\">Usually one<\/td><td class=\"has-text-align-center\" data-align=\"center\">Often many valid answers<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Consistency<\/td><td class=\"has-text-align-center\" data-align=\"center\">Same input gives the same output<\/td><td class=\"has-text-align-center\" data-align=\"center\">Outputs can vary between runs<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Main scoring<\/td><td class=\"has-text-align-center\" data-align=\"center\">Automated metrics<\/td><td class=\"has-text-align-center\" data-align=\"center\">Rubrics, LLM judges, and human review<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">What can change<\/td><td class=\"has-text-align-center\" data-align=\"center\">Mainly the model and data<\/td><td class=\"has-text-align-center\" data-align=\"center\">Model version, prompts, retrieval, and tools<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">In practice, this means LLM evaluation should use clear scoring rubrics, run each test case more than once, and keep a regression suite that re-runs automatically whenever a prompt, retrieval setting, or model version changes. When you <a href=\"https:\/\/www.xicom.biz\/ai-model-llm-fine-tuning-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">fine-tune an LLM<\/a>, compare it against the base model on the same suite to confirm the gains are real and that general abilities have not dropped.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Common_AI_Model_Evaluation_Mistakes\"><\/span>Common AI Model Evaluation Mistakes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Relying on accuracy for imbalanced data.<\/strong> If 1% of transactions are fraud, a model that flags nothing is 99% accurate and completely useless.<\/li>\n\n\n\n<li><strong>Data leakage.<\/strong> Test data that overlaps with training data produces scores that collapse in production.<\/li>\n\n\n\n<li><strong>Judging by demos.<\/strong> A few impressive examples say little about performance across thousands of real requests.<\/li>\n\n\n\n<li><strong>Trusting public benchmarks alone.<\/strong> Your data, users, and edge cases are different.<\/li>\n\n\n\n<li><strong>Ignoring segments.<\/strong> Averages hide poor performance for specific regions, languages, or customer groups.<\/li>\n\n\n\n<li><strong>Leaving out latency and cost.<\/strong> The best-scoring model is not the best choice if it is too slow or expensive at scale.<\/li>\n\n\n\n<li><strong>Evaluating once.<\/strong> Data drift, new user behavior, and model updates can quietly reduce quality after launch.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Best_Practices_for_AI_Model_Evaluation\"><\/span>Best Practices for AI Model Evaluation<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-best-practices.webp\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"556\" src=\"https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-best-practices-1024x556.webp\" alt=\"Practices for AI Model Evaluation\" class=\"wp-image-15670\" srcset=\"https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-best-practices-1024x556.webp 1024w, https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-best-practices-300x163.webp 300w, https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-best-practices-768x417.webp 768w, https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-best-practices-150x81.webp 150w, https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/11\/ai-model-evaluation-best-practices.webp 1182w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Teams that evaluate AI models well tend to follow the same habits. These practices keep evaluation consistent as models, data, and prompts change.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Tie_Every_Metric_to_a_Business_Outcome\"><\/span>Tie Every Metric to a Business Outcome<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Translate model metrics into terms stakeholders understand, such as fraud losses prevented, support tickets resolved, or hours of manual review saved. This makes trade-off decisions clearer and easier to defend.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Maintain_and_Version_a_Golden_Dataset\"><\/span>Maintain and Version a Golden Dataset<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Keep a curated, versioned test set that grows over time. Add every important production failure to it so the same mistake is caught automatically in future releases.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Combine_Automated_and_Human_Evaluation\"><\/span>Combine Automated and Human Evaluation<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Use automated metrics and LLM judges for scale, and domain experts for nuance and high-risk cases. Review a sample of automated scores by hand regularly to make sure they still match human judgment.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Automate_Evaluation_in_Your_Release_Pipeline\"><\/span>Automate Evaluation in Your Release Pipeline<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Run the evaluation suite automatically on every model, prompt, or data change, and block releases that fall below agreed thresholds. Strong MLOps practices make this repeatable instead of a manual check before launch.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Monitor_Drift_After_Deployment\"><\/span>Monitor Drift After Deployment<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Track input data drift with measures such as the population stability index (PSI), watch prediction distributions, and collect user feedback signals such as corrections, thumbs-down ratings, and escalations to a human. Set alerts so the team investigates before users notice a drop.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Document_Results_for_Governance\"><\/span>Document Results for Governance<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Record what was tested, with which data, and with what results in model cards or evaluation reports. Frameworks such as the NIST AI Risk Management Framework and ISO\/IEC 42001 expect this kind of evidence, and <a href=\"https:\/\/www.xicom.biz\/ai-governance-consulting-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI governance consulting<\/a> can help you set it up in a way that satisfies auditors.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Popular_Tools_for_AI_Model_Evaluation\"><\/span>Popular Tools for AI Model Evaluation<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">You do not need to build evaluation tooling from scratch. Widely used options include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>scikit-learn:<\/strong> standard metrics for classification, regression, and clustering.<\/li>\n\n\n\n<li><strong>MLflow and Weights &amp; Biases:<\/strong> experiment tracking and side-by-side model comparison.<\/li>\n\n\n\n<li><strong>Evidently AI:<\/strong> data drift and model quality monitoring.<\/li>\n\n\n\n<li><strong>RAGAS:<\/strong> metrics for RAG pipelines, such as faithfulness and context precision.<\/li>\n\n\n\n<li><strong>DeepEval and promptfoo:<\/strong> test suites for LLM applications and prompts.<\/li>\n\n\n\n<li><strong>LangSmith:<\/strong> tracing and evaluation for LLM apps and agents.<\/li>\n<\/ul>\n\n\n<section class=\"inquireBlock text-center mt-3\">\n<div class=\"capTxt new\">Need a Reliable Way to Test Your AI Models?<\/div>\n<div class=\"smallTxt new mt-0 mb-3\">From golden datasets to automated LLM evaluation and drift monitoring, our team builds evaluation into your AI pipeline.<\/div>\n<div class=\"contact-bttn\"><a href=\"https:\/\/www.xicom.biz\/contact\/\">Get In Touch Now!<\/a><\/div>\n<\/section>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Knowing how to evaluate AI models is what separates a promising prototype from a dependable product. Define success in business terms, test on representative data, choose metrics that fit the task, and look beyond quality scores to fairness, robustness, latency, and cost. Then keep measuring after launch, because a model that works today can drift tomorrow.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">With 20+ years of software delivery and 1,800+ projects completed, <a href=\"https:\/\/www.xicom.biz\/\" target=\"_blank\" rel=\"noreferrer noopener\">Xicom<\/a> helps businesses build AI they can trust. Whether you need an independent <a href=\"https:\/\/www.xicom.biz\/ai-audit-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI audit<\/a> of an existing system or end-to-end <a href=\"https:\/\/www.xicom.biz\/ai-development-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI development services<\/a> with evaluation built in from day one, our team can help.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"FAQs\"><\/span>FAQs<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1762670000001\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"How_do_you_evaluate_an_AI_model\"><\/span>How do you evaluate an AI model?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>To evaluate an AI model, define business success criteria, test the model on held-out data that reflects real usage, and measure task-specific metrics such as precision, recall, or faithfulness. Then check robustness, fairness, latency, and cost, validate with real users through shadow or A\/B testing, and monitor the model continuously in production.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1762670000002\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"What_are_the_most_important_AI_model_evaluation_metrics\"><\/span>What are the most important AI model evaluation metrics?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>It depends on the task. Classification models use precision, recall, F1 score, and AUC-ROC. Regression models use MAE, RMSE, and R squared. LLMs and RAG systems use faithfulness, answer relevance, and hallucination rate. Every production model should also be measured on latency and cost per request.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1762670000003\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"Why_is_accuracy_not_enough_to_evaluate_an_AI_model\"><\/span>Why is accuracy not enough to evaluate an AI model?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Accuracy can be misleading when classes are imbalanced. If only 1% of transactions are fraud, a model that never flags fraud is still 99% accurate. Precision, recall, and F1 score show how well the model handles the cases that actually matter.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1762670000004\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"How_do_you_evaluate_a_large_language_model\"><\/span>How do you evaluate a large language model?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>LLMs are evaluated with a mix of reference-based metrics, rubric-based scoring by human experts or an LLM judge, and task metrics such as faithfulness, correctness, and hallucination rate. Teams also run safety and red-team tests, repeat test cases to account for varied outputs, and re-run a regression suite whenever prompts or model versions change.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1762670000005\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"What_is_LLM-as-a-judge\"><\/span>What is LLM-as-a-judge?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>LLM-as-a-judge is an evaluation method where a strong language model scores another model&#8217;s outputs against a defined rubric. It scales much better than human review, but judge models can be biased toward longer or self-similar answers, so their scores should be calibrated against a sample of human ratings.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1762670000006\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"What_is_model_drift_and_how_do_you_detect_it\"><\/span>What is model drift and how do you detect it?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Model drift is the gradual drop in model performance as real-world data or user behavior changes after deployment. It is detected by monitoring input data distributions with measures such as the population stability index, tracking prediction patterns and quality metrics, and watching user feedback signals.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1762670000007\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"How_often_should_AI_models_be_re-evaluated\"><\/span>How often should AI models be re-evaluated?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>AI models should be monitored continuously in production and fully re-evaluated whenever the model is retrained, a prompt or retrieval setting changes, the provider releases a new model version, or monitoring shows a meaningful drop in quality.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"Quick AnswerTo evaluate an AI model, define what success means for the business, test the model on held-out data that reflects real usage, and measure task-specific metrics such as precision and recall for classification or faithfulness for LLMs. Then check robustness, fairness, latency, and cost, and keep monitoring the model in production. No single metric","protected":false},"author":1,"featured_media":15667,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[454],"tags":[1260,1261,1259],"class_list":["post-15664","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-ai-model-evaluation","tag-ai-model-evaluation-metrices","tag-how-to-evaluate-ai-models"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.xicom.biz\/blog\/wp-json\/wp\/v2\/posts\/15664","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.xicom.biz\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.xicom.biz\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.xicom.biz\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.xicom.biz\/blog\/wp-json\/wp\/v2\/comments?post=15664"}],"version-history":[{"count":3,"href":"https:\/\/www.xicom.biz\/blog\/wp-json\/wp\/v2\/posts\/15664\/revisions"}],"predecessor-version":[{"id":15681,"href":"https:\/\/www.xicom.biz\/blog\/wp-json\/wp\/v2\/posts\/15664\/revisions\/15681"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.xicom.biz\/blog\/wp-json\/wp\/v2\/media\/15667"}],"wp:attachment":[{"href":"https:\/\/www.xicom.biz\/blog\/wp-json\/wp\/v2\/media?parent=15664"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.xicom.biz\/blog\/wp-json\/wp\/v2\/categories?post=15664"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.xicom.biz\/blog\/wp-json\/wp\/v2\/tags?post=15664"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}