{"id":352,"date":"2026-04-11T09:22:34","date_gmt":"2026-04-11T09:22:34","guid":{"rendered":"https:\/\/www.promptposition.com\/blog\/does-chatgpt-give-the-same-answers-to-everyone\/"},"modified":"2026-04-11T09:22:48","modified_gmt":"2026-04-11T09:22:48","slug":"does-chatgpt-give-the-same-answers-to-everyone","status":"publish","type":"post","link":"https:\/\/www.promptposition.com\/blog\/does-chatgpt-give-the-same-answers-to-everyone\/","title":{"rendered":"Does ChatGPT Give the Same Answers to Everyone? A Guide"},"content":{"rendered":"<p>A familiar scene is playing out in marketing teams right now.<\/p>\n<p>A CEO forwards a ChatGPT screenshot that recommends a competitor. Later that day, a sales lead pastes in nearly the same prompt and gets an answer that praises your product instead. Both people ask the same question. Both are looking at the same platform. The outputs don&#039;t match.<\/p>\n<p>That gap creates a simple but urgent question. <strong>Does ChatGPT give the same answers to everyone?<\/strong><\/p>\n<p>The short answer is no. The more useful answer is that this variation isn&#039;t random in the way marketers usually mean it. It&#039;s a built-in property of how these systems work, and it creates a new search environment where brands need monitoring, testing, and a response plan.<\/p>\n<h2>The Question Every Brand is Asking About ChatGPT<\/h2>\n<p>The brand problem isn&#039;t theoretical anymore.<\/p>\n<p>A buyer asks ChatGPT for the best payroll software, best agency partner, best cybersecurity platform, or best CRM for a startup. Your brand might appear in one answer, disappear in another, and get described differently across both. The issue isn&#039;t only visibility. It&#039;s <strong>positioning<\/strong>.<\/p>\n<p>That matters because ChatGPT reached <strong>800 million weekly active users by December 2025, up from 100 million in November 2023<\/strong>, according to <a href=\"https:\/\/www.ekamoira.com\/blog\/does-chatgpt-give-the-same-answers-to-everyone\" target=\"_blank\" rel=\"noopener\">Ekamoira&#039;s analysis of ChatGPT response variability<\/a>. At that scale, inconsistent AI answers stop being a curiosity and become a brand management issue.<\/p>\n<h3>What marketers are seeing<\/h3>\n<p>In practice, teams usually notice the problem in one of three ways:<\/p>\n<ul>\n<li><strong>Executive escalation:<\/strong> A leader sees a bad answer and wants to know why the model &quot;got it wrong.&quot;<\/li>\n<li><strong>Sales friction:<\/strong> Prospects repeat AI-generated claims that don&#039;t match your messaging.<\/li>\n<li><strong>Competitive drift:<\/strong> A rival starts appearing in recommendation prompts where your brand used to show up.<\/li>\n<\/ul>\n<p>None of those are solved by asking whether AI is good or bad. The useful question is whether your team can measure the pattern.<\/p>\n<blockquote>\n<p>A single screenshot is not a brand diagnosis. It&#039;s one sample from a moving system.<\/p>\n<\/blockquote>\n<p>That&#039;s the shift many teams still haven&#039;t made. They treat one answer as truth; the task is understanding the range of likely answers your market sees.<\/p>\n<h3>Which answer is the one<\/h3>\n<p>Usually, both are real.<\/p>\n<p>AI systems don&#039;t work like a fixed FAQ page that serves the same line every time. They generate responses in context. So one screenshot does not represent the answer. It&#039;s the spread of outputs buyers are likely to encounter across prompts, sessions, and models.<\/p>\n<p>For a modern marketing leader, this changes the job in two ways:<\/p>\n<ol>\n<li><strong>You need to know where your brand appears.<\/strong><\/li>\n<li><strong>You need to know how the model describes you when it does.<\/strong><\/li>\n<\/ol>\n<p>If your team is still treating AI answers as edge-case behavior, you&#039;re already behind the channel.<\/p>\n<h2>The Core Reason ChatGPT Answers Differ<\/h2>\n<p>The clearest way to understand this is to stop thinking of ChatGPT as a database.<\/p>\n<p>A database retrieves stored information. ChatGPT generates text one word at a time. It predicts what token should come next based on probabilities, then continues that process until it forms a response. That is why the same prompt can produce slightly different phrasing, different examples, or a different structure.<\/p>\n<p><figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/www.promptposition.com\/blog\/wp-content\/uploads\/2026\/04\/does-chatgpt-give-the-same-answers-to-everyone-generative-ai-scaled.jpg\" alt=\"A diagram comparing database retrieval of existing information versus the word-by-word generative creation process of AI.\" \/><\/figure><\/p>\n<h3>Retrieval versus generation<\/h3>\n<p>Search engines historically trained marketers to expect repeatability. You search a phrase, and the engine retrieves indexed pages according to a ranking system.<\/p>\n<p>ChatGPT behaves differently. It belongs to the family of <a href=\"https:\/\/titanblue.com.au\/what-are-large-language-models\/\" target=\"_blank\" rel=\"noopener\">Large Language Models (LLMs)<\/a>, which generate language rather than fetch a stored answer. That&#039;s why even a factual prompt can return the same core idea with different wording.<\/p>\n<p>A helpful analogy is this:<\/p>\n\n<figure class=\"wp-block-table\"><table><tr>\n<th>System<\/th>\n<th>What it does<\/th>\n<th>What that means for marketers<\/th>\n<\/tr>\n<tr>\n<td>Database or static knowledge base<\/td>\n<td>Retrieves an existing record<\/td>\n<td>Results are comparatively stable<\/td>\n<\/tr>\n<tr>\n<td>Generative AI model<\/td>\n<td>Builds an answer token by token<\/td>\n<td>Output can vary even when the prompt doesn&#039;t<\/td>\n<\/tr>\n<\/table><\/figure>\n<h3>Why the wording changes<\/h3>\n<p>ChatGPT&#039;s responses are <strong>probabilistic, not deterministic<\/strong>. It computes a probability distribution for the next token, and that distribution is influenced by settings including temperature, often in the <strong>0.7 to 1.0<\/strong> range, which helps explain why identical prompts can produce lexically different responses, as outlined in TechRadar&#039;s explanation of how ChatGPT generates answers.<\/p>\n<p>For marketers, that technical point has a practical consequence. You are not competing for one frozen answer. You are competing for presence inside a probability space.<\/p>\n<h3>Why perfect consistency isn&#039;t the goal<\/h3>\n<p>Many teams hear this and immediately ask how to force the model to always say the same thing.<\/p>\n<p>That usually isn&#039;t realistic at scale. Models are designed to generate, adapt, and personalize. The better goal is narrower and more useful:<\/p>\n<ul>\n<li><strong>Increase the chance your brand appears<\/strong><\/li>\n<li><strong>Improve the quality of that appearance<\/strong><\/li>\n<li><strong>Reduce harmful variation on high-value prompts<\/strong><\/li>\n<\/ul>\n<blockquote>\n<p><strong>Practical rule:<\/strong> Treat AI visibility like share of voice in a fluid channel, not like a fixed ranking in classic search.<\/p>\n<\/blockquote>\n<p>That mindset leads to better decisions. It shifts effort away from chasing one screenshot and toward building durable coverage across the prompts that influence buyers.<\/p>\n<h2>The 5 Key Factors Driving Answer Variation<\/h2>\n<p>Once you accept that the model generates instead of retrieves, the next question is what causes one answer to differ from another. There are several drivers, but five matter most in day-to-day marketing work.<\/p>\n<p>A 2024 peer-reviewed study found that, even with repeated queries, <strong>GPT-4 achieved 44.9% consistency in selecting the correct answer across all rounds, with 85.7% overall accuracy, compared with 57.7% accuracy for GPT-3.5<\/strong>, showing that better models can still vary materially across repeated runs, as reported in the <a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10969490\/\" target=\"_blank\" rel=\"noopener\">PMC study on ChatGPT accuracy and consistency<\/a>.<\/p>\n<p><figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/www.promptposition.com\/blog\/wp-content\/uploads\/2026\/04\/does-chatgpt-give-the-same-answers-to-everyone-chatgpt-factors.jpg\" alt=\"An infographic titled The 5 Key Factors Driving Answer Variation in ChatGPT explaining how AI responses change.\" \/><\/figure><\/p>\n<h3>Model version matters more than often assumed<\/h3>\n<p>GPT-3.5 and GPT-4 don&#039;t behave the same way. Newer releases often produce longer, more structured, and more accurate responses. They can also make different choices about which brands, examples, and source patterns to emphasize.<\/p>\n<p>If your team compares screenshots taken from different model versions, you&#039;re not comparing like with like.<\/p>\n<h3>Conversation history changes the answer<\/h3>\n<p>This is the one that catches non-technical users.<\/p>\n<p>If the chat already contains messages about budget sensitivity, enterprise security, startup growth, or B2B software, the next answer will often reflect that context. In practice, the same prompt inside two different chat threads can function like two different queries.<\/p>\n<p>That&#039;s one reason broad prompt testing matters. A single isolated run misses how real users interact with the tool.<\/p>\n<h3>Prompt phrasing changes the candidate set<\/h3>\n<p>Small wording shifts can change output more than many teams expect.<\/p>\n<p>&quot;Best CRM for startups&quot; is not the same prompt as &quot;Which CRM should a seed-stage SaaS company choose?&quot; One is broader and list-oriented. The other adds buyer stage and business model. Those differences can alter which brands are mentioned and what criteria the model prioritizes.<\/p>\n<p>A useful related concept is query fan-out. If you&#039;re tracking only one surface-level prompt, you&#039;re undercounting the range of ways buyers ask the same question. This is why teams working on AI search should understand <a href=\"https:\/\/www.promptposition.com\/blog\/query-fan-out\/\">query fan-out in LLM visibility workflows<\/a>.<\/p>\n<h3>User settings add hidden personalization<\/h3>\n<p>Custom instructions and memory can shape responses without being obvious in the interface.<\/p>\n<p>One user may have told the model to prefer concise answers, avoid certain vendors, or focus on local options. Another may have a long history of asking about enterprise software. Those settings alter outputs in ways that aren&#039;t visible from a screenshot alone.<\/p>\n<h3>Platform conditions affect how answers are delivered<\/h3>\n<p>Usage conditions can influence response length and level of detail. Teams often notice that outputs become shorter or less developed during peak periods.<\/p>\n<p>That doesn&#039;t mean the brand narrative is entirely different. It does mean the presentation can change enough to affect buyer perception, especially when lists become shorter and fewer vendors make the cut.<\/p>\n<blockquote>\n<p>If your brand only appears when the answer is long, you don&#039;t have durable visibility.<\/p>\n<\/blockquote>\n<h2>Seeing is Believing How to Test AI Variability<\/h2>\n<p>The fastest way to understand does chatgpt give the same answers to everyone is to run the test yourself.<\/p>\n<p>Don&#039;t start with your brand name. Start with two prompt types. One should be factual and objective. The other should be comparative and subjective. That contrast shows where volatility is low and where it becomes a real brand issue.<\/p>\n<p>Response variability scales with query type. <strong>Objective queries tend to show only 2 to 5% wording variance, while subjective prompts can diverge by 25 to 40%<\/strong>, according to <a href=\"https:\/\/www.airops.com\/blog\/does-chatgpt-give-the-same-answers-to-everyone\" target=\"_blank\" rel=\"noopener\">Airops&#039; breakdown of ChatGPT response variability<\/a>.<\/p>\n<p><figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/www.promptposition.com\/blog\/wp-content\/uploads\/2026\/04\/does-chatgpt-give-the-same-answers-to-everyone-ai-comparison-scaled.jpg\" alt=\"A magnifying glass compares two different artificial intelligence responses about how to make a quick homemade snack.\" \/><\/figure><\/p>\n<h3>Run this simple two-prompt test<\/h3>\n<p>Open fresh chats and repeat each prompt several times.<\/p>\n<p><strong>Prompt A, objective<\/strong><\/p>\n<blockquote>\n<p>What is the capital of France?<\/p>\n<\/blockquote>\n<p><strong>Prompt B, subjective<\/strong><\/p>\n<blockquote>\n<p>What are the best CRMs for a startup in 2026?<\/p>\n<\/blockquote>\n<p>What you should look for:<\/p>\n<ul>\n<li><strong>For Prompt A:<\/strong> The core answer should stay stable. Wording may shift slightly.<\/li>\n<li><strong>For Prompt B:<\/strong> Brand mentions may change. Order may change. Reasons may change. The overall framing may change.<\/li>\n<\/ul>\n<h3>A better brand test<\/h3>\n<p>After that, move to queries a buyer would ask.<\/p>\n<p>Use prompts like these:<\/p>\n<ol>\n<li><strong>Category prompt<\/strong><blockquote>\n<p>What are the best project management tools for a mid-sized marketing team?<\/p>\n<\/blockquote>\n<\/li>\n<li><strong>Comparison prompt<\/strong><blockquote>\n<p>Compare [your brand] with [competitor] for enterprise reporting.<\/p>\n<\/blockquote>\n<\/li>\n<li><strong>Recommendation prompt<\/strong><blockquote>\n<p>Which vendor should a fast-growing ecommerce brand choose for customer support automation?<\/p>\n<\/blockquote>\n<\/li>\n<li><strong>Trust prompt<\/strong><blockquote>\n<p>Which companies are most reliable in [your category]?<\/p>\n<\/blockquote>\n<\/li>\n<\/ol>\n<p>Run each prompt in a fresh chat, then run it again after a different conversation context. Note what changes.<\/p>\n<h3>What to capture in a spreadsheet<\/h3>\n<p>Don&#039;t overcomplicate the first pass. Track:<\/p>\n\n<figure class=\"wp-block-table\"><table><tr>\n<th>Field<\/th>\n<th>What to record<\/th>\n<\/tr>\n<tr>\n<td>Prompt<\/td>\n<td>Exact text used<\/td>\n<\/tr>\n<tr>\n<td>Brand mentions<\/td>\n<td>Which brands appear<\/td>\n<\/tr>\n<tr>\n<td>Rank order<\/td>\n<td>Where your brand appears in the list<\/td>\n<\/tr>\n<tr>\n<td>Sentiment<\/td>\n<td>Positive, neutral, or negative framing<\/td>\n<\/tr>\n<tr>\n<td>Source cues<\/td>\n<td>Any sites, articles, or references the model points toward<\/td>\n<\/tr>\n<tr>\n<td>Response shape<\/td>\n<td>List, comparison, narrative, or recommendation<\/td>\n<\/tr>\n<\/table><\/figure>\n<p>Dedicated tooling becomes useful here. If your team is already tracking AI surfaces beyond ChatGPT, an <a href=\"https:\/\/www.promptposition.com\/blog\/ai-overview-tracker\/\">AI Overview tracker workflow<\/a> gives a good model for organizing repeated prompt monitoring across volatile answer environments.<\/p>\n<blockquote>\n<p>Run high-value prompts more than once. One response tells you what happened once. Repeated runs tell you what buyers are likely to see.<\/p>\n<\/blockquote>\n<h2>Why Inconsistent AI Answers Are a Major Brand Risk<\/h2>\n<p>Most marketing teams still underestimate the commercial impact here.<\/p>\n<p>They assume AI answers are a content problem, when they&#039;re also a <strong>reputation<\/strong>, <strong>demand capture<\/strong>, and <strong>category framing<\/strong> problem. If ChatGPT describes your brand differently across users, your market doesn&#039;t receive one message. It receives a set of shifting messages.<\/p>\n<h3>The risk is bigger than missing one mention<\/h3>\n<p>A missing mention hurts. A wrong mention can hurt more.<\/p>\n<p>If the model places your product in the wrong category, misstates your strengths, or repeats stale framing, your team now has to fix a perception problem before sales can even start the primary conversation.<\/p>\n<p>That challenge is similar to what support leaders face when customers get different answers from different agents. Halo AI&#039;s write-up on <a href=\"https:\/\/www.haloagents.ai\/blog\/support-quality-consistency-problems\" target=\"_blank\" rel=\"noopener\">support quality consistency problems<\/a> is useful because it highlights the same operational issue from another angle. Inconsistent responses erode trust even when each individual answer sounds plausible.<\/p>\n<h3>Four business risks that show up fast<\/h3>\n<h4>Inconsistent messaging<\/h4>\n<p>Your website says one thing. Your sales deck says another. The AI summary says a third.<\/p>\n<p>That gap confuses buyers, especially in crowded categories where differentiation depends on precise wording.<\/p>\n<h4>Competitive displacement<\/h4>\n<p>If a model recommends competitors in comparison prompts, you don&#039;t just lose visibility. You lose the framing battle at the point of evaluation.<\/p>\n<p>That is different from ranking below a rival in classic search. In AI, the model often compresses the shortlist for the user.<\/p>\n<h4>Reputational drag<\/h4>\n<p>Even a partly accurate answer can create a problem if the tone is off. Buyers remember simple narratives. &quot;Enterprise but hard to use&quot; or &quot;good for SMBs, not for scale&quot; can stick long after the user leaves the chat.<\/p>\n<h4>Misinformation at the edge<\/h4>\n<p>Some AI outputs contain errors, unsupported claims, or outdated assumptions. That makes the channel risky for brands that don&#039;t actively watch it.<\/p>\n<h3>Why screenshot-based management fails<\/h3>\n<p>Many teams react to AI answers one screenshot at a time. That approach doesn&#039;t scale.<\/p>\n<p>You need trend visibility. You need to know whether a negative framing is isolated or recurring. You need to know whether a competitor is gaining share across recommendation prompts. You need to know which prompts drive the most harmful differences.<\/p>\n<p>A useful starting point is learning <a href=\"https:\/\/www.promptposition.com\/blog\/how-to-measure-brand-perception\/\">how to measure brand perception in AI-generated environments<\/a>. Once you frame AI outputs as measurable perception data, the problem becomes manageable.<\/p>\n<h2>From Monitoring to Mitigation A Strategic Framework<\/h2>\n<p>Most brands can&#039;t control what ChatGPT says in every session. They can control how seriously they monitor the channel and how deliberately they influence the inputs that shape visibility.<\/p>\n<p>The strongest teams treat AI search the way mature SEO teams treat rankings, snippets, reviews, and brand SERPs. They benchmark, observe, adjust, and repeat.<\/p>\n<p><figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/www.promptposition.com\/blog\/wp-content\/uploads\/2026\/04\/does-chatgpt-give-the-same-answers-to-everyone-ai-visibility-scaled.jpg\" alt=\"A circular diagram illustrating the cycle of AI search visibility, featuring monitoring, analysis, adjustment, and benchmarking steps.\" \/><\/figure><\/p>\n<h3>Start with a prompt set that reflects buyer intent<\/h3>\n<p>Don&#039;t monitor vanity prompts first.<\/p>\n<p>Begin with prompts tied to evaluation, comparison, trust, use case fit, and alternatives. These are the prompts that shape pipeline, not just awareness.<\/p>\n<p>A practical starter set includes:<\/p>\n<ul>\n<li><strong>Category entry prompts<\/strong> such as best tools, top platforms, and recommended vendors<\/li>\n<li><strong>Comparison prompts<\/strong> where buyers weigh your brand against named competitors<\/li>\n<li><strong>Use-case prompts<\/strong> tied to role, industry, company size, or specific jobs to be done<\/li>\n<li><strong>Credibility prompts<\/strong> around reliability, support, pricing fit, and implementation complexity<\/li>\n<\/ul>\n<h3>Track visibility, sentiment, and source reliance over time<\/h3>\n<p>Operating discipline matters here.<\/p>\n<p>One useful option is <strong>promptposition<\/strong>, which tracks visibility, sentiment, competitor positioning, verbatim quotes, and underlying sources across ChatGPT, Claude, Gemini, and Perplexity. That kind of setup helps teams move from isolated screenshots to repeatable measurement.<\/p>\n<p>You don&#039;t need a huge process at the start. You do need consistency.<\/p>\n<h3>Build around source influence, not only prompt influence<\/h3>\n<p>A lot of teams focus only on prompt engineering. That&#039;s useful, but it isn&#039;t enough.<\/p>\n<p>Models often reflect patterns found in source material across the web. If the most cited articles, reviews, listings, and comparison pages don&#039;t support your positioning, changing the prompt won&#039;t fix the deeper issue.<\/p>\n<p>This is why files and publishing standards that help models interpret your site are becoming part of the playbook. For teams thinking about machine-readable site guidance, this overview of <a href=\"https:\/\/www.promptposition.com\/blog\/llms-txt\/\">llms.txt and how it fits AI discovery<\/a> is worth reviewing.<\/p>\n<h3>Look for deterministic windows<\/h3>\n<p>Variability is normal, but some low-entropy, objective prompts can still produce token-for-token identical outputs, as discussed in the <a href=\"https:\/\/community.openai.com\/t\/why-does-chatgpt-give-exactly-the-same-response-to-some-prompts\/330680\" target=\"_blank\" rel=\"noopener\">OpenAI community thread on exact same responses for some prompts<\/a>. For marketers, that&#039;s useful because it suggests some prompts can be made more stable than others.<\/p>\n<p>That doesn&#039;t mean you&#039;ll get perfect uniformity across every user and context. It does mean some prompt classes are more controllable.<\/p>\n<blockquote>\n<p>Stable prompt zones are where defensive and offensive AI search strategy meet. They&#039;re often narrow, but they&#039;re valuable.<\/p>\n<\/blockquote>\n<h3>What works and what doesn&#039;t<\/h3>\n\n<figure class=\"wp-block-table\"><table><tr>\n<th>Works<\/th>\n<th>Doesn&#039;t work<\/th>\n<\/tr>\n<tr>\n<td>Monitoring repeated runs of important prompts<\/td>\n<td>Judging visibility from one screenshot<\/td>\n<\/tr>\n<tr>\n<td>Tracking competitor mentions alongside your own<\/td>\n<td>Looking only at your brand name<\/td>\n<\/tr>\n<tr>\n<td>Improving source presence across the web<\/td>\n<td>Relying only on prompt wording<\/td>\n<\/tr>\n<tr>\n<td>Testing fresh chats and context-loaded chats<\/td>\n<td>Assuming one session reflects all users<\/td>\n<\/tr>\n<tr>\n<td>Prioritizing high-intent prompts<\/td>\n<td>Chasing every possible prompt equally<\/td>\n<\/tr>\n<\/table><\/figure>\n<p>The teams that win here don&#039;t expect ChatGPT to become static. They build systems that let them see movement early and respond before competitors lock in the narrative.<\/p>\n<hr>\n<p>If your team needs a practical way to track how AI models describe your brand, <a href=\"https:\/\/www.promptposition.com\">promptposition<\/a> gives you a structured view of visibility, sentiment, competitor mentions, verbatim responses, and source patterns across major LLMs so you can monitor change and act on it.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A familiar scene is playing out in marketing teams right now. A CEO forwards a ChatGPT screenshot that recommends a competitor. Later that day, a sales lead pastes in nearly&#8230;<\/p>\n","protected":false},"author":1,"featured_media":351,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[30,21,188,187,46],"class_list":["post-352","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-ai-search-visibility","tag-brand-management","tag-chatgpt-consistency","tag-does-chatgpt-give-the-same-answers-to-everyone","tag-promptposition"],"_links":{"self":[{"href":"https:\/\/www.promptposition.com\/blog\/wp-json\/wp\/v2\/posts\/352","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.promptposition.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.promptposition.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.promptposition.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.promptposition.com\/blog\/wp-json\/wp\/v2\/comments?post=352"}],"version-history":[{"count":1,"href":"https:\/\/www.promptposition.com\/blog\/wp-json\/wp\/v2\/posts\/352\/revisions"}],"predecessor-version":[{"id":357,"href":"https:\/\/www.promptposition.com\/blog\/wp-json\/wp\/v2\/posts\/352\/revisions\/357"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.promptposition.com\/blog\/wp-json\/wp\/v2\/media\/351"}],"wp:attachment":[{"href":"https:\/\/www.promptposition.com\/blog\/wp-json\/wp\/v2\/media?parent=352"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.promptposition.com\/blog\/wp-json\/wp\/v2\/categories?post=352"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.promptposition.com\/blog\/wp-json\/wp\/v2\/tags?post=352"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}