{"id":1696,"date":"2025-09-04T11:10:56","date_gmt":"2025-09-04T11:10:56","guid":{"rendered":"https:\/\/www.heliosz.ai\/blogs\/?p=1696"},"modified":"2025-11-07T12:02:31","modified_gmt":"2025-11-07T12:02:31","slug":"fixing-ai-model-hallucinations","status":"publish","type":"post","link":"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/","title":{"rendered":"AI Model Hallucinations Why They Occur and How to Mitigate Them"},"content":{"rendered":"\n<p>Artificial intelligence (AI) is one of the upcoming facets of modern technology.&nbsp;&nbsp; From virtual assistants and chatbots to legal research and medical diagnosis, artificial intelligence (AI) is changing our communications, businesses, and way of life.&nbsp;&nbsp; Though there are benefits to these techniques, there are also drawbacks.&nbsp;&nbsp; One disquieting limitation of large language models (LLMs) and generative AI models is &#8220;AI hallucinations.&#8221;&nbsp;<\/p>\n\n\n\n<p>An artificial intelligence hallucination is a situation where a model generates data that appears to be correct but is indeed incorrect, misleading, or useless.&nbsp; Due to this issue, AI systems can become less reliable and dependable and even hazardous in important domains such as banking, law, and medicine.&nbsp;<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_74 ez-toc-wrap-left ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#What_are_AI_Hallucinations\" >What are AI Hallucinations?&nbsp;&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#Why_Do_AI_Hallucinations_Happen\" >Why Do AI Hallucinations Happen?&nbsp;&nbsp;<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#a_Limitations_of_Training_Data\" >a) Limitations of Training Data&nbsp;&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#b_Absence_of_External_Reality_Grounding\" >b) Absence of External Reality Grounding&nbsp;&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#c_Excessive_Generalization_of_Patterns_Learned\" >c) Excessive Generalization of Patterns Learned&nbsp;&nbsp;&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#d_Prompt_Ambiguity_and_Model_Misinterpretation\" >d) Prompt Ambiguity and Model Misinterpretation&nbsp;&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#e_Language_Models_Inherent_Probabilistic_Character\" >e) Language Models&#8217; Inherent Probabilistic Character&nbsp;&nbsp;<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#Strategies_to_Mitigate_AI_Hallucinations\" >Strategies to Mitigate AI Hallucinations&nbsp;<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#a_Boost_the_Quality_of_Training_Data\" >a) Boost the Quality of Training Data&nbsp;&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#b_Include_Outside_Information_Sources\" >b) Include Outside Information Sources&nbsp;&nbsp;&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#c_Employ_Human_Feedback_in_Reinforcement_Learning_RLHF\" >c) Employ Human Feedback in Reinforcement Learning (RLHF)&nbsp;&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#d_Put_Fact-Checking_and_Validation_Layers_into_Practice\" >d) Put Fact-Checking and Validation Layers into Practice&nbsp;&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#e_Contextual_Framing_and_Prompt_Engineering\" >e) Contextual Framing and Prompt Engineering&nbsp;&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#f_Employ_Domain-Specific_Adjustment\" >f) Employ Domain-Specific Adjustment&nbsp;&nbsp;&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#g_Promote_Openness_and_Model_Explainability\" >g) Promote Openness and Model Explainability&nbsp;&nbsp;&nbsp;<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.heliosz.ai\/blog\/fixing-ai-model-hallucinations\/#Conclusion\" >Conclusion&nbsp;&nbsp;&nbsp;<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_are_AI_Hallucinations\"><\/span>What are AI Hallucinations?&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Artificial intelligence hallucinations are possible when an AI model creates content that seems coherent and well-structured but is actually factually in error, inconsistent, or entirely fictional.&nbsp; Fabricating historical events and data, skewing scientific facts, or making references are just a couple of the many ways in which these hallucinations could present.&nbsp;<\/p>\n\n\n\n<p>In natural language processing (NLP), hallucinations are especially common in large language models such as GPT, BERT, and so on.&nbsp; For example, a language model can proudly cite a fictional research article or provide a totally fabricated name if asked for the name of book\u2019s author.&nbsp; Despite the fact that these responses are appropriate to the context and grammatically correct, they could be entirely nonsensical.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Why_Do_AI_Hallucinations_Happen\"><\/span>Why Do AI Hallucinations Happen?&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"368\" src=\"https:\/\/www.heliosz.ai\/blogs\/wp-content\/uploads\/2025\/09\/AI-Hallucinations-Explained-03-1-1024x368.jpg\" alt=\"AI Hallucinations Reason\" class=\"wp-image-1701\" srcset=\"https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2025\/09\/AI-Hallucinations-Explained-03-1-1024x368.jpg 1024w, https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2025\/09\/AI-Hallucinations-Explained-03-1-300x108.jpg 300w, https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2025\/09\/AI-Hallucinations-Explained-03-1-768x276.jpg 768w, https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2025\/09\/AI-Hallucinations-Explained-03-1-1536x552.jpg 1536w, https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2025\/09\/AI-Hallucinations-Explained-03-1-2048x736.jpg 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"a_Limitations_of_Training_Data\"><\/span>a) Limitations of Training Data&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The quality of the data upon which AI models are trained dictates their quality. If the training dataset is biased, incomplete, dated, or contains errors, then the model can take in and mimic those flaws. Because large language models train on large corpora scraped off the internet, they always take in both correct and incorrect content. Instead of &#8220;knowing&#8221; <a href=\"https:\/\/www.heliosz.ai\/blog\/gen-ai-use-cases-in-retail\/\" title=\"\">what is actually true<\/a> when requested to generate a response, the model mimics patterns in its training.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"b_Absence_of_External_Reality_Grounding\"><\/span>b) Absence of External Reality Grounding&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Unless specifically built to do so, the majority of language models are not automatically linked to external databases or real-time data. This discrepancy results in &#8220;ungrounded generation,&#8221; where the model synthesizes responses from training data instead of cross-referencing them with current or reliable sources. Hallucinations are more likely when there is no external grounding, particularly in dynamic domains like current affairs or quickly changing technologies.&nbsp;&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"c_Excessive_Generalization_of_Patterns_Learned\"><\/span>c) Excessive Generalization of Patterns Learned&nbsp;&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Patterns allow language models to generalize. A model may overgeneralize associations if it observes that particular phrases or structures frequently occur together. For instance, the model may produce references that look similar even if there isn&#8217;t a paper with that format if it has seen a lot of academic citations. These are referred to as &#8220;template-based hallucinations&#8221; and are particularly prevalent when creating academic or technical texts.&nbsp;&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"d_Prompt_Ambiguity_and_Model_Misinterpretation\"><\/span>d) Prompt Ambiguity and Model Misinterpretation&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The format in which a user asks a question has a strong effect on the output of the model. A poorly phrased or incomplete prompt will cause the model to misinterpret and hallucinate. Well-formatted prompts can also hallucinate if the model doesn&#8217;t have adequate context or belief in the subject. In essence, the model can &#8220;guess&#8221; the best answer from partial input, resulting in fabricated or hallucinated answers.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"e_Language_Models_Inherent_Probabilistic_Character\"><\/span>e) Language Models&#8217; Inherent Probabilistic Character&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Fundamentally, language <a href=\"https:\/\/www.heliosz.ai\/blog\/challenges-of-scaling-machine-learning-models\/\" title=\"\">models use learned<\/a> probabilities to predict the subsequent word in sequence. Because of their probabilistic nature, they are sophisticated pattern matchers rather than deterministic truth engines. As a result, the model may &#8220;hallucinate&#8221; information to preserve fluency and coherence when confidence in the next likely token is low, particularly on specialized or unclear topics.&nbsp;&nbsp;<\/p>\n\n\n\n<p>Strategies to Mitigate AI Hallucinations&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Strategies_to_Mitigate_AI_Hallucinations\"><\/span>Strategies to Mitigate AI Hallucinations&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"488\" src=\"https:\/\/www.heliosz.ai\/blogs\/wp-content\/uploads\/2025\/09\/AI-Hallucinations-Explained-04-1-1024x488.jpg\" alt=\"Strategies to mitigate AI Hallucinations\" class=\"wp-image-1706\" srcset=\"https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2025\/09\/AI-Hallucinations-Explained-04-1-1024x488.jpg 1024w, https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2025\/09\/AI-Hallucinations-Explained-04-1-300x143.jpg 300w, https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2025\/09\/AI-Hallucinations-Explained-04-1-768x366.jpg 768w, https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2025\/09\/AI-Hallucinations-Explained-04-1-1536x732.jpg 1536w, https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2025\/09\/AI-Hallucinations-Explained-04-1-2048x976.jpg 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"a_Boost_the_Quality_of_Training_Data\"><\/span>a) Boost the Quality of Training Data&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The probability of hallucinations can be decreased by improving the training dataset&#8217;s quality, diversity, and accuracy. This entails eliminating false information, selecting datasets that are specific to a given domain, and preventing the use of unreliable sources twice. Reliable model outputs are greatly enhanced by clean, high-quality data.&nbsp;&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"b_Include_Outside_Information_Sources\"><\/span>b) Include Outside Information Sources&nbsp;&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Linking language models to search engines, real-time databases, or APIs can assist in grounding their answers in current, accurate data. One such method is retrieval-augmented generation (RAG), in which models obtain pertinent information from reliable sources prior to producing text. The amount of ungrounded content is greatly decreased by this technique.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"c_Employ_Human_Feedback_in_Reinforcement_Learning_RLHF\"><\/span>c) Employ Human Feedback in Reinforcement Learning (RLHF)&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>AI outputs are more in line with human values and factual accuracy when models are trained using reinforcement learning and human feedback. During training, human reviewers steer the model away from inaccurate or deceptive content, increasing the likelihood that the model will produce reliable information later on.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"d_Put_Fact-Checking_and_Validation_Layers_into_Practice\"><\/span>d) Put Fact-Checking and Validation Layers into Practice&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Hallucinations can be prevented prior to content reaching the end user by implementing a post-generation fact-checking layer. In order to detect discrepancies or unverified claims, AI text can be run through verification algorithms or cross-checked against knowledge graphs. This is particularly useful in academic, business, and journalistic environments.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"e_Contextual_Framing_and_Prompt_Engineering\"><\/span>e) Contextual Framing and Prompt Engineering&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A well-crafted prompt can eliminate uncertainty and direct the model toward more precise results. The rate of hallucinations can be decreased by giving the model clear instructions, enough context, and requests that it cite sources or validate assertions. An increasingly crucial ability for the safe and efficient deployment of LLMs is <a href=\"https:\/\/www.heliosz.ai\/blog\/fine-tuning-vs-prompt-engineering\/\" title=\"\">prompt engineering<\/a>.&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"f_Employ_Domain-Specific_Adjustment\"><\/span>f) Employ Domain-Specific Adjustment&nbsp;&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>By constraining the scope of the model and enriching its knowledge in a specific domain, fine-tuning language models with domain-specific data\u2014such as court documents, medical records, or scientific papers can reduce hallucinations. For industrial-strength AI applications where accuracy is paramount, this approach is highly desirable.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"g_Promote_Openness_and_Model_Explainability\"><\/span>g) Promote Openness and Model Explainability&nbsp;&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Creating models with explainable AI (XAI) capabilities can assist users in comprehending the process and motivation behind the creation of a specific output. Users can detect and fix hallucinated content more readily if they are able to follow the model&#8217;s logic or pinpoint the influences of its sources.&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion&nbsp;&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The growth of language models and generative AI is strongly hindered by AI hallucinations.&nbsp; These seemingly reliable but unfounded outcomes can mislead customers, harm companies, and influence decisions in various industries.&nbsp; We can reduce these issues by understanding the root causes, which can involve factors such as model conception and data constraints, and using careful risk-reduction practices.&nbsp;<\/p>\n\n\n\n<p>We must make sure that AI-generated material is correct, understandable, and reliable as it more and more pervades our lives.\u00a0 To build a well-regulated AI environment that harvests the gain from smart systems without their periodic but damaging mistakes, developers, corporations, and buyers must work together.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Artificial intelligence (AI) is one of the upcoming facets of modern technology.&nbsp;&nbsp; From virtual assistants and chatbots to legal research [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1703,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[16],"tags":[],"class_list":["post-1696","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/posts\/1696","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/comments?post=1696"}],"version-history":[{"count":15,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/posts\/1696\/revisions"}],"predecessor-version":[{"id":2078,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/posts\/1696\/revisions\/2078"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/media\/1703"}],"wp:attachment":[{"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/media?parent=1696"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/categories?post=1696"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/tags?post=1696"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}