{"id":123182,"date":"2026-07-23T12:15:17","date_gmt":"2026-07-23T06:45:17","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=123182"},"modified":"2026-07-23T12:15:18","modified_gmt":"2026-07-23T06:45:18","slug":"how-to-reduce-claude-hallucinations","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/how-to-reduce-claude-hallucinations\/","title":{"rendered":"How to Reduce Claude Hallucinations with Better Prompts"},"content":{"rendered":"\n<p>Even the most advanced AI assistants can occasionally produce answers that sound convincing but are factually incorrect. This phenomenon, commonly known as <strong>AI hallucination<\/strong>, can lead to inaccurate code, fabricated citations, incorrect business insights, or misleading explanations if left unchecked.<\/p>\n\n\n\n<p>The good news is that hallucinations are often reduced\u2014not by changing the model\u2014but by improving how you write prompts. Clear instructions, sufficient context, and well-designed workflows help reduce Claude  Hallucinations  and produce more accurate, grounded, and reliable responses. In this article, you&#8217;ll learn why Claude hallucinates, common causes, practical prompt engineering techniques, and best practices to minimize hallucinations in real-world applications.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>TL;DR Summary Box<\/strong><\/h2>\n\n\n\n<ul>\n<li>Hallucinations are incorrect or unsupported AI-generated outputs.<\/li>\n\n\n\n<li>Better prompts improve accuracy by reducing ambiguity.<\/li>\n\n\n\n<li>Give Claude enough context instead of expecting it to guess.<\/li>\n\n\n\n<li>Use RAG for company documents and frequently changing information.<\/li>\n\n\n\n<li>Encourage Claude to acknowledge uncertainty instead of inventing answers.<\/li>\n\n\n\n<li>Always verify important technical, legal, medical, or financial information.<\/li>\n<\/ul>\n\n\n\n<p><em>Reduce Claude hallucinations by letting it say \u201cI don\u2019t know,\u201d grounding answers in sources, and requiring citations for every claim. Learn AI &amp; ML with HCL GUVI\u2019s<\/em><a href=\"https:\/\/www.guvi.in\/mlp\/artificial-intelligence-and-machine-learning?utm_source=blog&amp;utm_medium=hyperlink&amp;utm_campaign=how-to-reduce-claude-hallucinations\" target=\"_blank\" rel=\"noreferrer noopener\"><em> Artificial Intelligence and Machine Learning course<\/em><\/a><em>.<\/em><\/p>\n\n\n\n<div style=\"\n  margin: 40px 0;\n  border: 1px solid #d9efe2;\n  border-radius: 12px;\n  overflow: hidden;\n  box-shadow: 0 4px 12px rgba(0,0,0,0.05);\n  font-family: Arial, sans-serif;\n\">\n\n  <!-- Question -->\n  <div style=\"\n    background: #099f4e;\n    color: #ffffff;\n    padding: 18px 24px;\n  \">\n    <h2 style=\"\n      margin: 0;\n      font-size: 24px;\n      font-weight: 700;\n      line-height: 1.4;\n      color: #ffffff;\n    \">\n      How can you reduce Claude hallucinations with better prompts?\n    <\/h2>\n  <\/div>\n\n  <!-- Answer -->\n  <div style=\"\n    background: #f8fffb;\n    padding: 24px;\n  \">\n    <p style=\"\n      margin: 0;\n      color: #374151;\n      font-size: 16px;\n      line-height: 1.8;\n    \">\n      <strong>You can reduce Claude hallucinations<\/strong> by writing clear prompts, providing sufficient context, grounding responses with trusted reference material or Retrieval-Augmented Generation (RAG), encouraging Claude to acknowledge uncertainty instead of guessing, and verifying important outputs against authoritative sources. These practices help produce more accurate, reliable, and transparent AI responses.\n    <\/p>\n  <\/div>\n\n<\/div>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>What Are Claude Hallucinations?<\/strong><\/h1>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"940\" height=\"496\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/what-are-claude-hal.webp\" alt=\"\" class=\"wp-image-125355\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/what-are-claude-hal.webp 940w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/what-are-claude-hal-300x158.webp 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/what-are-claude-hal-768x405.webp 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/what-are-claude-hal-150x79.webp 150w\" sizes=\"(max-width: 940px) 100vw, 940px\" title=\"\"><\/figure>\n\n\n\n<p>A hallucination occurs when Claude generates information that appears plausible but is inaccurate, fabricated, or unsupported by reliable evidence.<\/p>\n\n\n\n<p>Examples include:<\/p>\n\n\n\n<ul>\n<li>Inventing <a href=\"https:\/\/www.guvi.in\/hub\/network-programming-with-python\/understanding-apis\/?utm_source=blog&amp;utm_medium=hyperlink&amp;utm_campaign=how-to-reduce-claude-hallucinations\" target=\"_blank\" data-type=\"link\" data-id=\"https:\/\/www.guvi.in\/hub\/network-programming-with-python\/understanding-apis\/%5C\" rel=\"noreferrer noopener\">API<\/a> parameters<\/li>\n\n\n\n<li>Misquoting documentation<\/li>\n\n\n\n<li>Fabricating research citations<\/li>\n\n\n\n<li>Incorrect code examples<\/li>\n\n\n\n<li>Wrong product specifications<\/li>\n\n\n\n<li>False historical details<\/li>\n<\/ul>\n\n\n\n<p>Hallucinations are a limitation of probabilistic language generation and can occur in any large language model.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>Why Do Hallucinations Happen?<\/strong><\/h1>\n\n\n\n<p>Claude predicts the most likely next tokens based on patterns learned during training. When a prompt lacks sufficient information or requests unavailable knowledge, the model may generate its best estimate instead of providing a definitive answer.<\/p>\n\n\n\n<p>Common causes include:<\/p>\n\n\n\n<ul>\n<li>Ambiguous prompts<\/li>\n\n\n\n<li>Missing context<\/li>\n\n\n\n<li>Outdated knowledge<\/li>\n\n\n\n<li>Overly broad questions<\/li>\n\n\n\n<li>Requests for unavailable information<\/li>\n\n\n\n<li>Pressure to answer every question<\/li>\n<\/ul>\n\n\n\n<p>\ud83d\udcca <strong>Data Point<\/strong><\/p>\n\n\n\n<p>Research across the <a href=\"https:\/\/www.guvi.in\/blog\/what-is-artificial-intelligence\/\" target=\"_blank\" data-type=\"link\" data-id=\"https:\/\/www.guvi.in\/blog\/what-is-artificial-intelligence\/\" rel=\"noreferrer noopener\">AI<\/a> industry shows that retrieval-based systems and well-structured prompts generally produce more reliable factual responses than relying solely on a model&#8217;s internal knowledge.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>How Do Better Prompts Reduce Hallucinations?<\/strong><\/h1>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"940\" height=\"529\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/how-do-better-prompts.webp\" alt=\"\" class=\"wp-image-125356\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/how-do-better-prompts.webp 940w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/how-do-better-prompts-300x169.webp 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/how-do-better-prompts-768x432.webp 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/how-do-better-prompts-150x84.webp 150w\" sizes=\"(max-width: 940px) 100vw, 940px\" title=\"\"><\/figure>\n\n\n\n<p>Good prompts reduce uncertainty by giving Claude clear instructions, relevant context, and defined expectations.<\/p>\n\n\n\n<p>Instead of:<\/p>\n\n\n\n<p>&#8220;Explain this API.&#8221;<\/p>\n\n\n\n<p>Try:<\/p>\n\n\n\n<p>&#8220;Based only on the API documentation below, explain how the authentication flow works. If the documentation doesn&#8217;t specify something, explicitly say it&#8217;s not documented.&#8221;<\/p>\n\n\n\n<p>This approach encourages grounded responses rather than educated guesses.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>1.Provide Sufficient Context<\/strong><\/h1>\n\n\n\n<p>The less Claude has to infer, the more reliable its responses are.<\/p>\n\n\n\n<p>Include:<\/p>\n\n\n\n<ul>\n<li>Relevant documentation<\/li>\n\n\n\n<li>Business rules<\/li>\n\n\n\n<li>User requirements<\/li>\n\n\n\n<li>Code snippets<\/li>\n\n\n\n<li>Error messages<\/li>\n\n\n\n<li>Desired output format<\/li>\n<\/ul>\n\n\n\n<p>Providing context is often more effective than asking Claude to &#8220;be more accurate.&#8221;<\/p>\n\n\n\n<p>\ud83d\udca1 <strong>Pro Tip<\/strong><\/p>\n\n\n\n<p>Treat your <a href=\"https:\/\/www.guvi.in\/blog\/what-is-prompt-tuning\/\" target=\"_blank\" rel=\"noreferrer noopener\">prompt <\/a>like a project brief. The clearer your instructions, the less room there is for incorrect assumptions.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>2. Ask Claude to Admit Uncertainty<\/strong><\/h1>\n\n\n\n<p>One effective prompting technique is explicitly allowing Claude to say when it doesn&#8217;t know something.<\/p>\n\n\n\n<p>For example:<\/p>\n\n\n\n<ul>\n<li>&#8220;If the answer isn&#8217;t supported by the provided information, say you don&#8217;t have enough information.&#8221;<\/li>\n\n\n\n<li>&#8220;Don&#8217;t make assumptions.&#8221;<\/li>\n\n\n\n<li>&#8220;Distinguish facts from estimates.&#8221;<\/li>\n<\/ul>\n\n\n\n<p>These instructions encourage transparency over speculation.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>3. Use Retrieval-Augmented Generation (RAG)<\/strong><\/h1>\n\n\n\n<p>When answers depend on current or proprietary information, Retrieval-Augmented Generation (RAG) is often the most reliable approach.<\/p>\n\n\n\n<p>RAG allows Claude to retrieve relevant information from trusted sources before generating a response.<\/p>\n\n\n\n<p>Ideal use cases include:<\/p>\n\n\n\n<ul>\n<li>Company documentation<\/li>\n\n\n\n<li>Product manuals<\/li>\n\n\n\n<li>Knowledge bases<\/li>\n\n\n\n<li>Internal policies<\/li>\n\n\n\n<li>Technical specifications<\/li>\n<\/ul>\n\n\n\n<p>Rather than relying on memory alone, Claude grounds its answers in retrieved content.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>4. Break Complex Tasks into Smaller Steps<\/strong><\/h1>\n\n\n\n<p>Long, multi-part prompts can increase the likelihood of mistakes.<\/p>\n\n\n\n<p>Instead of asking:<\/p>\n\n\n\n<p>&#8220;Design, implement, optimize, document, and test this application.&#8221;<\/p>\n\n\n\n<p>Break the work into stages:<\/p>\n\n\n\n<ol>\n<li>Design the architecture.<\/li>\n\n\n\n<li>Review the design.<\/li>\n\n\n\n<li>Generate code.<\/li>\n\n\n\n<li>Review the implementation.<\/li>\n\n\n\n<li>Optimize performance.<\/li>\n\n\n\n<li>Produce documentation.<\/li>\n<\/ol>\n\n\n\n<p>Smaller tasks are generally easier for the model to execute accurately.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>5. Ask for Sources or Supporting Reasoning<\/strong><\/h1>\n\n\n\n<p>When factual accuracy is important, request supporting references.<\/p>\n\n\n\n<p>For example:<\/p>\n\n\n\n<ul>\n<li>&#8220;Cite the official documentation when possible.&#8221;<\/li>\n\n\n\n<li>&#8220;Separate confirmed facts from assumptions.&#8221;<\/li>\n\n\n\n<li>&#8220;Identify any uncertainty.&#8221;<\/li>\n<\/ul>\n\n\n\n<p>This helps you evaluate the reliability of the response more effectively.<\/p>\n\n\n\n<p>\u26a0\ufe0f <strong>Warning<\/strong><\/p>\n\n\n\n<p>Requesting citations does not guarantee they are genuine. Always verify important references using authoritative sources.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>Prompt Patterns That Improve Accuracy<\/strong><\/h1>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"940\" height=\"529\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/prompt-patterns-accu.webp\" alt=\"\" class=\"wp-image-125357\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/prompt-patterns-accu.webp 940w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/prompt-patterns-accu-300x169.webp 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/prompt-patterns-accu-768x432.webp 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/prompt-patterns-accu-150x84.webp 150w\" sizes=\"(max-width: 940px) 100vw, 940px\" title=\"\"><\/figure>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Weak Prompt<\/strong><\/td><td><strong>Better Prompt<\/strong><\/td><\/tr><tr><td>Explain Kubernetes<\/td><td>Explain Kubernetes for backend developers with one deployment example<\/td><\/tr><tr><td>What&#8217;s the best database?<\/td><td>Compare PostgreSQL and MongoDB for an analytics platform<\/td><\/tr><tr><td>Write API code<\/td><td>Generate Python code using the API documentation below<\/td><\/tr><tr><td>Summarize this<\/td><td>Summarize the following document in five bullet points without adding new information<\/td><\/tr><tr><td>Answer this question<\/td><td>Answer only using the supplied reference material<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<div style=\"background-color: #099f4e; border: 3px solid #110053; border-radius: 12px; padding: 18px 22px; color: #FFFFFF; font-size: 18px; font-family: Montserrat, Helvetica, sans-serif; line-height: 1.6; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); max-width: 750px;\">\n\n  <strong style=\"font-size: 22px; color: #FFFFFF;\">\ud83d\udca1 Did You Know?<\/strong>\n  <br \/><br \/>\n\n  <strong style=\"color: #FFFFFF;\">Prompt engineering often has a greater impact on reducing AI hallucinations than simply switching to a larger language model.<\/strong> Providing Claude with clear instructions, relevant context, and trusted reference material helps generate more accurate, grounded, and reliable responses while reducing unsupported assumptions.\n\n<\/div>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>Common Prompting Mistakes<\/strong><\/h1>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Mistake<\/strong><\/td><td><strong>Better Practice<\/strong><\/td><\/tr><tr><td>Vague questions<\/td><td>Ask specific questions<\/td><\/tr><tr><td>Missing documentation<\/td><td>Provide reference material<\/td><\/tr><tr><td>Asking for unsupported facts<\/td><td>Request uncertainty when appropriate<\/td><\/tr><tr><td>Overloading a single prompt<\/td><td>Divide work into smaller steps<\/td><\/tr><tr><td>Assuming AI always knows<\/td><td>Verify important outputs<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>Real-World Example<\/strong><\/h1>\n\n\n\n<p>Imagine a software team using Claude to generate API integration code.<\/p>\n\n\n\n<p>Initially, developers ask:<\/p>\n\n\n\n<p>&#8220;Write the integration.&#8221;<\/p>\n\n\n\n<p>Claude generates code based on assumptions, including endpoints and parameters that don&#8217;t exist.<\/p>\n\n\n\n<p>The team then changes its prompt:<\/p>\n\n\n\n<p>&#8220;Use only the OpenAPI specification below. If any required endpoint or parameter is missing, state that the specification doesn&#8217;t provide enough information.&#8221;<\/p>\n\n\n\n<p>With the documentation included, Claude produces code that aligns with the API definition and explicitly identifies any gaps instead of inventing implementation details.<\/p>\n\n\n\n<p><em>Reduce Claude hallucinations by letting it say \u201cI don\u2019t know,\u201d grounding answers in sources, and requiring citations for every claim. Learn AI &amp; ML with HCL GUVI\u2019s<\/em><a href=\"https:\/\/www.guvi.in\/mlp\/artificial-intelligence-and-machine-learning?utm_source=blog&amp;utm_medium=hyperlink&amp;utm_campaign=how-to-reduce-claude-hallucinations\" target=\"_blank\" rel=\"noreferrer noopener\"><em> Artificial Intelligence and Machine Learning course<\/em><\/a><em>.<\/em><\/p>\n\n\n\n<p><strong>Hallucination Reduction Techniques Compared<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Technique<\/strong><\/td><td><strong>Reduces Hallucinations?<\/strong><\/td><td><strong>Best Use Case<\/strong><\/td><\/tr><tr><td>Better prompts<\/td><td>\u2705 Yes<\/td><td>General tasks<\/td><\/tr><tr><td>RAG<\/td><td>\u2705 Excellent<\/td><td>Company knowledge and current information<\/td><\/tr><tr><td>Tool Use<\/td><td>\u2705 Yes<\/td><td>Live APIs and business systems<\/td><\/tr><tr><td>Human review<\/td><td>\u2705 Essential<\/td><td>High-risk decisions<\/td><\/tr><tr><td>Prompt chaining<\/td><td>\u2705 Yes<\/td><td>Complex workflows<\/td><\/tr><tr><td>Verification against official sources<\/td><td>\u2705 Critical<\/td><td>Technical and compliance-sensitive tasks<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h1>\n\n\n\n<p>Hallucinations are an inherent challenge of working with large language models, but they are not something you have to accept as unavoidable. In many cases, the quality of Claude&#8217;s responses depends as much on the information you provide as on the model itself. Clear prompts, relevant context, Retrieval-Augmented Generation, Tool Use, and thoughtful workflow design all contribute to more reliable outputs.<\/p>\n\n\n\n<p>Rather than trying to force certainty from incomplete information, build AI applications that encourage transparency, provide trusted context, and include human oversight where accuracy matters most. By focusing on grounded prompting instead of guesswork, you can significantly improve the quality and trustworthiness of Claude&#8217;s responses.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>FAQs<\/strong><\/h1>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1783968901286\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">1. <strong>What is a Claude hallucination?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>A Claude hallucination is a response that contains incorrect, fabricated, or unsupported information while appearing confident and plausible.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783968947128\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">2. <strong>Can hallucinations be completely eliminated?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>No. Like other large language models, Claude can still produce inaccurate information. However, clear prompts, relevant context, RAG, and human verification can significantly reduce the likelihood of hallucinations.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783968964250\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">3. <strong>How do better prompts reduce hallucinations?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Better prompts reduce ambiguity by providing clear instructions, sufficient context, and defined expectations, making it less likely that Claude will rely on assumptions.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783968984148\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">4. <strong>Is RAG better than prompt engineering?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>They serve different purposes. Prompt engineering improves how instructions are communicated, while RAG provides access to current or proprietary information. Many production systems use both together.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783968995558\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">5. <strong>Should I ask Claude to cite its sources?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes, when appropriate. Requesting references or indicating uncertainty can improve transparency, but you should still verify citations using authoritative sources.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783969012959\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">6. <strong>Does Tool Use help reduce hallucinations?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes. Tool Use allows Claude to retrieve live information or interact with external systems, reducing the need to generate answers based solely on internal knowledge.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783969029337\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">7. <strong>Are hallucinations more common in technical tasks?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>They can occur in any domain, but technical tasks involving undocumented APIs, changing specifications, or proprietary information often benefit most from additional context and retrieval mechanisms.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Even the most advanced AI assistants can occasionally produce answers that sound convincing but are factually incorrect. This phenomenon, commonly known as AI hallucination, can lead to inaccurate code, fabricated citations, incorrect business insights, or misleading explanations if left unchecked. The good news is that hallucinations are often reduced\u2014not by changing the model\u2014but by improving [&hellip;]<\/p>\n","protected":false},"author":63,"featured_media":125354,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[933],"tags":[],"views":"34","authorinfo":{"name":"Vishalini Devarajan","url":"https:\/\/www.guvi.in\/blog\/author\/vishalini\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/Reduce-claude-hal-300x115.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/123182"}],"collection":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/users\/63"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=123182"}],"version-history":[{"count":6,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/123182\/revisions"}],"predecessor-version":[{"id":125641,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/123182\/revisions\/125641"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/125354"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=123182"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=123182"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=123182"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}