{"id":707,"date":"2025-08-30T18:52:27","date_gmt":"2025-08-31T01:52:27","guid":{"rendered":"https:\/\/telewizard.ai\/blog\/?p=707"},"modified":"2025-09-12T02:48:43","modified_gmt":"2025-09-12T09:48:43","slug":"how-to-test-ai-models-reliable-strategies-for-ai-applications","status":"publish","type":"post","link":"https:\/\/telewizard.ai\/blog\/en\/2025\/08\/30\/how-to-test-ai-models-reliable-strategies-for-ai-applications\/","title":{"rendered":"How to Test AI Models &amp; Reliable Strategies for AI Applications"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"597\" src=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184926\/image-6-1024x597.png\" alt=\"How to Test AI Models\" class=\"wp-image-714\" srcset=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184926\/image-6-1024x597.png 1024w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184926\/image-6-300x175.png 300w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184926\/image-6-768x448.png 768w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184926\/image-6.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>Testing AI models is a must to ensure reliability, performance, and also security. Learn proven testing strategies, methods, and tools for building trustworthy AI applications. Discover How to Test AI Models effectively for consistent results.<\/p>\n\n\n\n<p>Artificial intelligence (AI) powers growth across industries. Its reliability depends on how effectively we test AI models. Unlike traditional software testing, AI model testing needs a broader set of techniques to validate performance and adaptability. The testing process makes sure that an AI system performs consistently in different conditions. Testing AI models is an essential step in the development of high-quality AI applications, and also understanding how to test AI Models is key to building reliable systems.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why is AI Testing a Must?<\/strong><\/h2>\n\n\n\n<p><strong>AI testing<\/strong> is more than checking for errors. It&#8217;s about verifying that a <strong>machine learning model<\/strong> behaves as expected in real world scenarios. Developers must <strong>validate<\/strong> results in different use cases, predictions with the intended purpose. <strong>Model testing<\/strong> also exposes hidden weaknesses like <strong>bias in AI<\/strong>, adversarial vulnerabilities, or reduced accuracy under stress.<\/p>\n\n\n\n<p>Since <strong>AI applications<\/strong> integrate with other digital systems, <strong>integration testing<\/strong> becomes vital. The ability to <strong>evaluate<\/strong> how an <strong>AI model<\/strong> interacts with surrounding services determines overall system resilience. Ultimately, <strong>testing is essential<\/strong> to guarantee dependable <strong>artificial intelligence<\/strong> solutions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Core Testing Strategies for AI<\/strong><\/h2>\n\n\n\n<p>The nature of <strong>AI applications<\/strong> demands specialized <strong>testing strategies<\/strong>. <strong>Functional testing<\/strong> ensures a model has basic requirements, additional layers, like <strong>security testing<\/strong> and <strong>performance testing<\/strong> determine if systems can fight malicious inputs or heavy workloads. Some of the most important strategies are,<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Adversarial testing<\/strong>, exposing weaknesses by feeding unexpected or manipulated inputs.<\/li>\n\n\n\n<li><strong>Exploratory testing<\/strong>, investigating new scenarios without predefined test cases.<\/li>\n\n\n\n<li><strong>Automated testing<\/strong>, using <strong>testing tools<\/strong> and also scripts for repetitive checks.<\/li>\n\n\n\n<li><strong>System testing<\/strong>, evaluates how the full <strong>AI system<\/strong> behaves when integrated into larger workflows.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Testing Methods for Machine Learning Models<\/strong><\/h2>\n\n\n\n<p><strong>AI models<\/strong> change with new data. This dynamic behavior requires <strong>testing methodologies<\/strong>. Teams frequently do <strong>test cases<\/strong> to simulate user interactions, compare outputs with expected values and also track deviations.<\/p>\n\n\n\n<p>Important <strong>testing methods<\/strong> like,<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Box testing<\/strong> (white box and black box) to analyze structure and also performance.<\/li>\n\n\n\n<li><strong>Functional testing<\/strong>.<\/li>\n\n\n\n<li><strong>Stress testing<\/strong>.<\/li>\n\n\n\n<li><strong>Integration testing<\/strong>.<\/li>\n<\/ul>\n\n\n\n<p>These methods help organizations build <strong>reliable AI<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Challenges in Testing AI Systems<\/strong><\/h2>\n\n\n\n<p><strong>AI systems<\/strong> introduce challenges not found in<strong> traditional testing<\/strong>. Models are opaque, difficult to interpret results. The <strong>nature of AI applications<\/strong> requires ongoing oversight through <strong>comprehensive testing<\/strong> and also <strong>thorough testing<\/strong> strategies. Issues such as <strong>ethical AI<\/strong> and transparency are highlightable things.<\/p>\n\n\n\n<p><strong>Rigorous testing is a must <\/strong>to maintain trust in intelligent technologies. Without a structured <strong>testing approach<\/strong>, the most advanced <strong>language models<\/strong> or <strong>AI agents<\/strong> can deliver unreliable results.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Future Trends &amp; Advanced Methods for Testing Generative AI Systems and AI Applications<\/strong><\/h2>\n\n\n\n<p>The evolution of <strong>generative AI<\/strong> has introduced new challenges in <strong>model testing<\/strong>. analyzing creativity and also relevance of outputs.\u00a0<\/p>\n\n\n\n<p><strong>Testing generative AI<\/strong> means verifying that outputs are not only accurate but also meaningful, ethical and safe. Since <strong>large language models<\/strong> and image generators create new content, <strong>rigorous testing<\/strong> becomes crucial. Common practices like,<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Exploratory testing<\/strong><\/li>\n\n\n\n<li><strong>Adversarial testing<\/strong> to challenge outputs with biased or misleading prompts.<\/li>\n\n\n\n<li><strong>Functional testing<\/strong> to check generated content aligns with the application\u2019s purpose.<\/li>\n\n\n\n<li><strong>Automation testing<\/strong><\/li>\n<\/ul>\n\n\n\n<p>Developers can ensure <strong>testing helps<\/strong> eliminate errors for trustworthy results.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>AI Agents and Application Testing<\/strong><\/h2>\n\n\n\n<p>As <strong>AI agents<\/strong> gain popularity in industries, validating their behavior is a must. Testing these agents has <strong>integration testing<\/strong> with broader systems, communication with users, and also external tools. A testing platform should simulate real-world workflows, teams to observe adaptability.<\/p>\n\n\n\n<p>When evaluating an <strong>AI app<\/strong>, developers must check for decision making accuracy, responsiveness, and ethical boundaries. <strong>System testing<\/strong> verifies the complete lifecycle of agent operations. This <strong>comprehensive testing<\/strong> approach ensures <strong>reliable AI<\/strong> performance.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Frameworks, Tools, and Monitoring Programs<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Category<\/strong><\/td><td><strong>What it\u2019s for<\/strong><\/td><td><strong>Good fits<\/strong><\/td><td><strong>Notable examples<\/strong><\/td><\/tr><tr><td>ML testing &amp; data quality<\/td><td>pre\/post-training checks, data validation, model tests, drift reports<\/td><td>DS\/ML teams needing automated checks and visual reports<\/td><td><strong>Deepchecks<\/strong> \u2014 data &amp; model tests (classification\/regression\/LLM)<br><br><strong>Great Expectations (GX)<\/strong> \u2014 data quality validation (\u201cExpectations\u201d)<br><br><strong>Evidently, AI<\/strong> \u2014 drift detection, reports, and monitoring dashboards<br><br><strong>Alibi Detect<\/strong> (Seldon) \u2014 drift, outlier &amp; adversarial detection<br><a href=\"https:\/\/github.com\/deepchecks\/deepchecks?utm_source=chatgpt.com\">GitHub<\/a><a href=\"https:\/\/www.fuzzylabs.ai\/blog-post\/validation-deepchecks-vs-great-expectations?utm_source=chatgpt.com\">fuzzylabs.ai<\/a><a href=\"https:\/\/www.deepchecks.com\/best-tools-for-testing-machine-learning-algorithms\/?utm_source=chatgpt.com\">Deepchecks<\/a><\/td><\/tr><tr><td>Experiment tracking &amp; lifecycle<\/td><td>track runs\/metrics\/params, compare experiments, lineage, model registry<\/td><td>Any ML workflow<\/td><td>MLflow, Weights &amp; Biases (W&amp;B)<a href=\"https:\/\/markaicode.com\/mlflow-vs-weights-biases-ml-experiment-tracking\/?utm_source=chatgpt.com\"> Markaicode<\/a><\/td><\/tr><tr><td>Model monitoring \/ ML observability<\/td><td><strong>Deepchecks<\/strong> \u2014 data &amp; model tests (classification\/regression\/LLM)<br><br><strong>Great Expectations (GX)<\/strong> \u2014 data quality validation (\u201cExpectations\u201d)<br><br><strong>Evidently AI<\/strong> \u2014 drift detection, reports, and monitoring dashboards<br><br><strong>Alibi Detect<\/strong> (Seldon) \u2014 drift, outlier &amp; adversarial detection<br><a href=\"https:\/\/github.com\/deepchecks\/deepchecks?utm_source=chatgpt.com\">GitHub<\/a><a href=\"https:\/\/www.fuzzylabs.ai\/blog-post\/validation-deepchecks-vs-great-expectations?utm_source=chatgpt.com\">fuzzylabs.ai<\/a><a href=\"https:\/\/www.deepchecks.com\/best-tools-for-testing-machine-learning-algorithms\/?utm_source=chatgpt.com\">Deepchecks<\/a><\/td><td>MLOps\/Platform teams<\/td><td><strong>WhyLabs<br>Arize AI<br>Fiddler<br>Superwise<br>NannyML<\/strong> \u2014 post-deployment performance estimation without labels<br><a href=\"https:\/\/www.mlopscrew.com\/blog\/top-ml-monitoring-tools?utm_source=chatgpt.com\">mlopscrew.com<\/a><a href=\"https:\/\/www.ctipath.com\/articles\/ai-mlops\/compare-and-contrast-seldon-fiddler-and-arize-ai-for-ml-model-monitoring-for-enterprises\/?utm_source=chatgpt.com\">ctipath.com<\/a><\/td><\/tr><tr><td>General observability &amp; APM (useful for AI apps)<\/td><td>Infra\/app logs, metrics, traces, alerting, anomaly detection<\/td><td>Azure AI Foundry Observability, similar features exist across major clouds,<a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/concepts\/observability?utm_source=chatgpt.com\"> Microsoft Learn<\/a><\/td><td><strong>Datadog<br>Dynatrace<br>New Relic<br>Grafana \/ Prometheus<br>LogicMonitor<\/strong><br><a href=\"https:\/\/www.techradar.com\/pro\/datadog-network-monitoring-review?utm_source=chatgpt.com\">TechRadar+2TechRadar+2<\/a><a href=\"https:\/\/grafana.com\/blog\/2024\/07\/02\/identify-anomalies-outlier-detection-forecasting-how-grafana-cloud-uses-ai-ml-to-make-observability-easier\/?utm_source=chatgpt.com\">Grafana Labs<\/a><\/td><\/tr><tr><td>Cloud AI monitoring (GenAI &amp; LLM apps)<\/td><td>safety\/performance, prompt\/response logging, evals<\/td><td>teams building LLM\/agentic apps<\/td><td>Production drift, data quality, latency, incidents, real-time dashboards<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1) Deepchecks \u2014 run data &amp; model checks (Python)<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"597\" src=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184303\/image-1024x597.png\" alt=\"How to Test AI Models\" class=\"wp-image-708\" srcset=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184303\/image-1024x597.png 1024w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184303\/image-300x175.png 300w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184303\/image-768x448.png 768w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184303\/image.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2) Evidently \u2014 generate a drift report for a reference vs. the current dataset<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"597\" src=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184318\/image-1-1024x597.png\" alt=\"How to Test AI Models\" class=\"wp-image-709\" srcset=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184318\/image-1-1024x597.png 1024w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184318\/image-1-300x175.png 300w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184318\/image-1-768x448.png 768w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184318\/image-1.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3) MLflow \u2014 log params\/metrics\/artifacts for each training run<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"597\" src=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184327\/image-2-1024x597.png\" alt=\"How to Test AI Models\" class=\"wp-image-710\" srcset=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184327\/image-2-1024x597.png 1024w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184327\/image-2-300x175.png 300w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184327\/image-2-768x448.png 768w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184327\/image-2.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4) W&amp;B \u2014 track experiments and basic model monitoring<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"597\" src=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184335\/image-3-1024x597.png\" alt=\"\" class=\"wp-image-711\" srcset=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184335\/image-3-1024x597.png 1024w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184335\/image-3-300x175.png 300w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184335\/image-3-768x448.png 768w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184335\/image-3.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>5) Prometheus + Grafana \u2014 expose custom app\/model metrics and visualize<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"597\" src=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184343\/image-4-1024x597.png\" alt=\"\" class=\"wp-image-712\" srcset=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184343\/image-4-1024x597.png 1024w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184343\/image-4-300x175.png 300w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184343\/image-4-768x448.png 768w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184343\/image-4.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>6) Alibi Detect \u2014 deploy drift\/anomaly detection in production<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"597\" src=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184349\/image-5-1024x597.png\" alt=\"\" class=\"wp-image-713\" srcset=\"https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184349\/image-5-1024x597.png 1024w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184349\/image-5-300x175.png 300w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184349\/image-5-768x448.png 768w, https:\/\/telewizard-blog-offloaded-media.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/30184349\/image-5.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Ethical AI and Reliability Considerations<\/strong><\/h2>\n\n\n\n<p>Building <strong>ethical AI<\/strong> requires more than just technical evaluation. Testing must address<strong> bias in AI models<\/strong>, data fairness, and transparency in decision making. A structured process helps ensure that <strong>AI<\/strong> behaves responsibly.<\/p>\n\n\n\n<p><strong>Thorough testing<\/strong> frameworks ensure that outputs are unbiased and inclusive. This is especially true for healthcare, finance, and legal systems, where the <strong>reliability of AI models<\/strong> directly impacts human lives.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p><strong>Testing is essential to ensure<\/strong> the success of modern AI. From validating <strong>language models<\/strong> to securing <strong>AI-based applications<\/strong>, organizations must adopt <strong>comprehensive testing methodologies<\/strong>. A balanced <strong>testing approach<\/strong> that integrates <strong>automated testing, exploratory testing, stress testing, and also functional testing<\/strong> will build a <strong>reliable <a href=\"https:\/\/en.wikipedia.org\/wiki\/Artificial_intelligence\" target=\"_blank\" rel=\"noopener\" title=\"\">AI<\/a><\/strong> capable of meeting real-world demands. Read our other <a href=\"https:\/\/telewizard.ai\/blog\/en\/\" target=\"_blank\" rel=\"noopener\" title=\"\">blogs<\/a> to get more valuable information.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>FAQs<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Why is AI model testing important?<\/strong><\/h3>\n\n\n\n<p>AI model testing ensures reliability and performance. It validates that models deliver accurate results, work under stress, and also use smoothly with other systems, reducing risks in real-world applications.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>What are common AI testing strategies?<\/strong><\/h3>\n\n\n\n<p>Key strategies like adversarial testing, exploratory testing, automation testing, and also system testing. These approaches help share hidden weaknesses, scalability, and validate AI performance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>What challenges exist in testing AI systems?<\/strong><\/h3>\n\n\n\n<p>AI testing is complex due to opaque models, evolving behavior, and bias risks. Continuous monitoring is a must to maintain trust and reliability in AI applications.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Testing AI models is a must to ensure reliability, performance, and also security. Learn proven testing strategies, methods, and tools&hellip;<\/p>\n","protected":false},"author":1,"featured_media":714,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15],"tags":[569],"class_list":["post-707","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-enterprise-ai-solutions","tag-ai-model-testing"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/telewizard.ai\/blog\/wp-json\/wp\/v2\/posts\/707","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/telewizard.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/telewizard.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/telewizard.ai\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/telewizard.ai\/blog\/wp-json\/wp\/v2\/comments?post=707"}],"version-history":[{"count":4,"href":"https:\/\/telewizard.ai\/blog\/wp-json\/wp\/v2\/posts\/707\/revisions"}],"predecessor-version":[{"id":723,"href":"https:\/\/telewizard.ai\/blog\/wp-json\/wp\/v2\/posts\/707\/revisions\/723"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/telewizard.ai\/blog\/wp-json\/wp\/v2\/media\/714"}],"wp:attachment":[{"href":"https:\/\/telewizard.ai\/blog\/wp-json\/wp\/v2\/media?parent=707"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/telewizard.ai\/blog\/wp-json\/wp\/v2\/categories?post=707"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/telewizard.ai\/blog\/wp-json\/wp\/v2\/tags?post=707"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}