{"id":7181,"date":"2025-12-20T12:41:07","date_gmt":"2025-12-20T12:41:07","guid":{"rendered":"https:\/\/aicamp.so\/blog\/?p=7181"},"modified":"2025-12-22T05:56:34","modified_gmt":"2025-12-22T05:56:34","slug":"why-same-ai-models-produce-different-results","status":"publish","type":"post","link":"https:\/\/aicamp.so\/blog\/why-same-ai-models-produce-different-results\/","title":{"rendered":"If the AI Model Is the Same, Why Do Outcomes Look So Different?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"7181\" class=\"elementor elementor-7181\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-31a80da e-flex e-con-boxed e-con e-parent\" data-id=\"31a80da\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-983843a elementor-widget elementor-widget-text-editor\" data-id=\"983843a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<blockquote><p><em data-start=\"3591\" data-end=\"3692\">This series is written for CIOs and IT leaders responsible for AI rollout in growing organizations.<\/em><\/p><\/blockquote>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-45ed2ef elementor-widget elementor-widget-text-editor\" data-id=\"45ed2ef\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"257\" data-end=\"365\">After publishing the <a href=\"https:\/\/aicamp.so\/blog\/why-ai-adoption-fails\/\">first article<\/a> in this series, a few CIOs replied with a variation of the same question.<\/p><p data-start=\"367\" data-end=\"463\">\u201cIf the underlying AI model is the same, why do outcomes differ so much across tools and teams?\u201d<\/p><p data-start=\"465\" data-end=\"509\">It\u2019s a fair question and an important one.<\/p><p data-start=\"511\" data-end=\"764\">On paper, many platforms claim access to the same GPT-class models. Leadership assumes that if the model is identical, results should be identical too. So when outputs vary, the instinct is to blame prompting skill, user maturity, or adoption readiness.<\/p><p data-start=\"766\" data-end=\"824\">What we\u2019ve learned from working closely with SMEs is this:<\/p><p data-start=\"826\" data-end=\"872\"><strong data-start=\"826\" data-end=\"872\">Model parity does not mean outcome parity.<\/strong><\/p><p data-start=\"874\" data-end=\"999\">In fact, focusing only on the model is one of the fastest ways to misdiagnose what\u2019s really happening inside an organization.<\/p><p data-start=\"1001\" data-end=\"1026\">This article unpacks why.<\/p><p data-start=\"1001\" data-end=\"1026\"><em>Read previous article : <a class=\"row-title\" href=\"https:\/\/aicamp.so\/blog\/wp-admin\/post.php?post=7170&amp;action=edit\" aria-label=\"\u201cWhy AI Adoption Fails in Small and Medium Enterprises\u201d (Edit)\">Why AI Adoption Fails in Small and Medium Enterprises<\/a><\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cc26259 elementor-widget elementor-widget-text-editor\" data-id=\"cc26259\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-start=\"1033\" data-end=\"1083\">The \u201csame model\u201d assumption breaks down quickly<\/h2><p data-start=\"1085\" data-end=\"1222\">At a technical level, it\u2019s true:<br data-start=\"1117\" data-end=\"1120\" \/>If two platforms are accessing the same underlying model version, the base intelligence is comparable.<\/p><p data-start=\"1224\" data-end=\"1267\">But AI systems do not operate in isolation.<\/p><p data-start=\"1269\" data-end=\"1285\">They respond to:<\/p><ul><li data-start=\"1288\" data-end=\"1295\">Context<\/li><li data-start=\"1298\" data-end=\"1310\">Instructions<\/li><li data-start=\"1313\" data-end=\"1319\">Memory<\/li><li data-start=\"1322\" data-end=\"1333\">Constraints<\/li><li data-start=\"1336\" data-end=\"1350\">Usage patterns<\/li><li data-start=\"1353\" data-end=\"1384\">Organizational inputs over time<\/li><\/ul><p data-start=\"1386\" data-end=\"1444\">Last month, a CIO put it bluntly during a working session:<\/p><blockquote data-start=\"1446\" data-end=\"1529\"><p data-start=\"1448\" data-end=\"1529\">The model isn\u2019t the issue. It\u2019s everything wrapped around it that we can\u2019t see.<\/p><\/blockquote><p data-start=\"1531\" data-end=\"1579\">That insight captures the core misunderstanding.<\/p><p data-start=\"1581\" data-end=\"1672\">The model is only one component of the system your employees are actually interacting with.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eaa1514 elementor-widget elementor-widget-text-editor\" data-id=\"eaa1514\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-start=\"1679\" data-end=\"1733\">AI output is shaped more by context than capability<\/h2><p data-start=\"1735\" data-end=\"1788\">In early experimentation, most AI usage looks simple:<\/p><ul><li data-start=\"1791\" data-end=\"1799\">One user<\/li><li data-start=\"1802\" data-end=\"1812\">One prompt<\/li><li data-start=\"1815\" data-end=\"1825\">One output<\/li><\/ul><p data-start=\"1827\" data-end=\"1876\">At that scale, differences are barely noticeable.<\/p><p data-start=\"1878\" data-end=\"1951\">But once AI is used across teams, the <strong data-start=\"1916\" data-end=\"1933\">context layer<\/strong> becomes decisive.<\/p><p data-start=\"1953\" data-end=\"1970\">Context includes:<\/p><ul><li data-start=\"1973\" data-end=\"2014\">What instructions persist across sessions<\/li><li data-start=\"2017\" data-end=\"2061\">What prior conversations influence responses<\/li><li data-start=\"2064\" data-end=\"2110\">What documents or knowledge bases are attached<\/li><li data-start=\"2113\" data-end=\"2145\">What guardrails exist (or don\u2019t)<\/li><li data-start=\"2148\" data-end=\"2200\">What the AI is allowed to remember, reuse, or ignore<\/li><\/ul><p data-start=\"2202\" data-end=\"2351\">Two employees using the \u201csame model\u201d can receive vastly different outputs simply because they are operating inside different contextual environments.<\/p><p data-start=\"2353\" data-end=\"2413\">And in most SMEs, that context is accidental not designed.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3f5d743 elementor-widget elementor-widget-text-editor\" data-id=\"3f5d743\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-start=\"2420\" data-end=\"2466\">Why SMEs experience inconsistent AI quality<\/h2><p data-start=\"2468\" data-end=\"2503\">Here\u2019s a pattern we see repeatedly.<\/p><p data-start=\"2505\" data-end=\"2520\">A team reports:<\/p><ul><li data-start=\"2523\" data-end=\"2555\">AI works great for some people<\/li><li data-start=\"2558\" data-end=\"2586\">Others say it\u2019s unreliable<\/li><li data-start=\"2589\" data-end=\"2620\">Outputs don\u2019t feel consistent<\/li><li data-start=\"2623\" data-end=\"2662\">We don\u2019t fully trust it for decisions<\/li><\/ul><p data-start=\"2664\" data-end=\"2714\">Leadership often assumes this is a training issue.<\/p><p data-start=\"2716\" data-end=\"2764\">In reality, it\u2019s usually a <strong data-start=\"2743\" data-end=\"2763\">structural issue<\/strong>.<\/p><p data-start=\"2766\" data-end=\"2782\">Different teams:<\/p><ul><li data-start=\"2785\" data-end=\"2806\">Use different prompts<\/li><li data-start=\"2809\" data-end=\"2841\">Start from different assumptions<\/li><li data-start=\"2844\" data-end=\"2868\">Share context informally<\/li><li data-start=\"2871\" data-end=\"2905\">Solve the same problem in parallel<\/li><li data-start=\"2908\" data-end=\"2956\">Lose learnings when people leave or change roles<\/li><\/ul><p data-start=\"2958\" data-end=\"3011\">AI becomes powerful in pockets, but brittle at scale.<\/p><p data-start=\"3013\" data-end=\"3052\">One IT leader recently described it as:<\/p><blockquote data-start=\"3054\" data-end=\"3142\"><p data-start=\"3056\" data-end=\"3142\">We don\u2019t have an AI problem. We have ten different versions of AI happening at once.<\/p><\/blockquote><p data-start=\"3144\" data-end=\"3249\">That fragmentation is invisible until the organization tries to rely on AI for more than experimentation.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4b97a93 elementor-widget elementor-widget-text-editor\" data-id=\"4b97a93\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-start=\"3256\" data-end=\"3296\">The hidden risk: unseen data exposure<\/h2><p data-start=\"3298\" data-end=\"3368\">The second misconception tied to \u201csame model\u201d thinking is data safety.<\/p><p data-start=\"3370\" data-end=\"3385\">CIOs often ask:<\/p><ul><li data-start=\"3388\" data-end=\"3420\">Is our data used for training?<\/li><li data-start=\"3423\" data-end=\"3440\">Is it retained?<\/li><li data-start=\"3443\" data-end=\"3467\">Where is it processed?<\/li><\/ul><p data-start=\"3469\" data-end=\"3520\">Those are valid questions but they\u2019re incomplete.<\/p><p data-start=\"3522\" data-end=\"3551\">What matters just as much is:<\/p><ul><li data-start=\"3554\" data-end=\"3601\">Who can share sensitive context unintentionally<\/li><li data-start=\"3604\" data-end=\"3642\">Where prompts and files live after use<\/li><li data-start=\"3645\" data-end=\"3683\">Whether outputs are reused responsibly<\/li><li data-start=\"3686\" data-end=\"3730\">Whether teams understand what <em data-start=\"3716\" data-end=\"3721\">not<\/em> to input<\/li><\/ul><p data-start=\"3732\" data-end=\"3815\">Even when models themselves are governed correctly, <strong data-start=\"3784\" data-end=\"3814\">usage patterns create risk<\/strong>.<\/p><p data-start=\"3817\" data-end=\"3840\">We\u2019ve seen cases where:<\/p><ul><li data-start=\"3843\" data-end=\"3907\">Sensitive context was pasted repeatedly because it \u201cworked once\u201d<\/li><li data-start=\"3910\" data-end=\"3966\">Prompts containing internal logic were shared externally<\/li><li data-start=\"3969\" data-end=\"4027\">Outputs were reused without knowing their original context<\/li><\/ul><p data-start=\"4029\" data-end=\"4083\">None of this was malicious. All of it was preventable.<\/p><p data-start=\"4085\" data-end=\"4164\">The issue wasn\u2019t the model. It was the absence of a shared operating framework.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d237394 elementor-widget elementor-widget-text-editor\" data-id=\"d237394\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-start=\"4171\" data-end=\"4220\">Why \u201cprompt training\u201d alone doesn\u2019t solve this<\/h2><p data-start=\"4222\" data-end=\"4282\">Many organizations respond by investing in prompt workshops.<\/p><p data-start=\"4284\" data-end=\"4320\">These are useful but insufficient.<br \/>Prompt skill improves individual outcomes. It does not fix organizational drift.<\/p><p data-start=\"4404\" data-end=\"4429\">Without shared standards:<\/p><ul><li data-start=\"4432\" data-end=\"4455\">Prompts decay over time<\/li><li data-start=\"4458\" data-end=\"4488\">Good practices don\u2019t propagate<\/li><li data-start=\"4491\" data-end=\"4515\">Bad habits scale quietly<\/li><li data-start=\"4518\" data-end=\"4542\">Knowledge remains tribal<\/li><\/ul><p data-start=\"4544\" data-end=\"4655\">AI maturity is not achieved by making everyone a power user. It\u2019s achieved by <strong data-start=\"4622\" data-end=\"4654\">making good usage repeatable<\/strong>.<\/p><p data-start=\"4657\" data-end=\"4681\">That requires structure.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bfe2264 elementor-widget elementor-widget-text-editor\" data-id=\"bfe2264\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-start=\"4688\" data-end=\"4741\">The real differentiator: how AI is operationalized<\/h2><p data-start=\"4743\" data-end=\"4821\">Once CIOs step back from model comparisons, a clearer evaluation lens emerges.<\/p><p data-start=\"4823\" data-end=\"4849\">The real questions become:<\/p><ul><li data-start=\"4852\" data-end=\"4898\">How is AI context created, stored, and reused?<\/li><li data-start=\"4901\" data-end=\"4941\">How do teams build on each other\u2019s work?<\/li><li data-start=\"4944\" data-end=\"5000\">How do leaders see what\u2019s working without micromanaging?<\/li><li data-start=\"5003\" data-end=\"5059\">How are boundaries enforced without slowing people down?<\/li><\/ul><p data-start=\"5061\" data-end=\"5134\">These are not model questions. They are platform and operating questions.<\/p><p data-start=\"5136\" data-end=\"5253\">The organizations that move fastest are not chasing newer models. They are designing <strong data-start=\"5221\" data-end=\"5252\">how AI fits into daily work<\/strong>.<\/p><p data-start=\"5255\" data-end=\"5282\">One CIO summarized it well:<\/p><blockquote data-start=\"5284\" data-end=\"5393\"><p data-start=\"5286\" data-end=\"5393\">We stopped asking which AI was smartest and started asking which system we could actually trust at scale.<\/p><\/blockquote>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1f7238e elementor-widget elementor-widget-text-editor\" data-id=\"1f7238e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-start=\"5400\" data-end=\"5450\">Why outcomes diverge even with identical models<\/h2><p data-start=\"5452\" data-end=\"5472\">To make it explicit:<\/p><p data-start=\"5474\" data-end=\"5513\">You can use the same GPT model through:<\/p><ul><li data-start=\"5516\" data-end=\"5536\">A personal interface<\/li><li data-start=\"5539\" data-end=\"5557\">A shared workspace<\/li><li data-start=\"5560\" data-end=\"5590\">A governed enterprise platform<\/li><\/ul><p data-start=\"5592\" data-end=\"5629\">And get completely different results.<\/p><p data-start=\"5631\" data-end=\"5658\">Because outcomes depend on:<\/p><ul><li data-start=\"5661\" data-end=\"5679\">Context continuity<\/li><li data-start=\"5682\" data-end=\"5694\">Prompt reuse<\/li><li data-start=\"5697\" data-end=\"5718\">Knowledge integration<\/li><li data-start=\"5721\" data-end=\"5731\">Visibility<\/li><li data-start=\"5734\" data-end=\"5744\">Guardrails<\/li><li data-start=\"5747\" data-end=\"5761\">Feedback loops<\/li><\/ul><p data-start=\"5763\" data-end=\"5871\">When those are missing, AI feels unpredictable. When they\u2019re present, AI feels reliable even conservative.<\/p><p data-start=\"5873\" data-end=\"5934\">That\u2019s the paradox many SMEs experience without realizing it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0a500b4 elementor-widget elementor-widget-text-editor\" data-id=\"0a500b4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2>\ud83d\udcd8 Read More in This Series<\/h2><ol><li><em><a class=\"row-title\" href=\"https:\/\/aicamp.so\/blog\/why-ai-adoption-fails\/\" aria-label=\"\u201cWhy AI Adoption Fails in Small and Medium Enterprises\u201d (Edit)\">Why AI Adoption Fails in Small and Medium Enterprises<\/a><\/em><\/li><li><em><a class=\"row-title\" href=\"https:\/\/aicamp.so\/blog\/how-cios-should-evaluate-ai-platforms\/\" aria-label=\"\u201cHow CIOs Should Evaluate AI Platforms for Employee Use\u201d (Edit)\">How CIOs Should Evaluate AI Platforms for Employee Use<\/a>\u00a0<\/em><\/li><li><em><a class=\"row-title\" href=\"https:\/\/aicamp.so\/blog\/structured-ai-rollout-for-employees\/\" aria-label=\"\u201cStructuring AI Rollout for Employees: A Practical Guide for CIOs\u201d (Edit)\">Structuring AI Rollout for Employees: A Practical Guide for CIOs<\/a>\u00a0<\/em><\/li><li><em><a class=\"row-title\" href=\"https:\/\/aicamp.so\/blog\/ai-rollout-roadmap-sme\/\" aria-label=\"\u201cThe Complete AI Rollout Roadmap for SMEs: From Evaluation to Deployment\u201d (Edit)\">The Complete AI Rollout Roadmap for SMEs: From Evaluation to Deployment<\/a>\u00a0<\/em><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-67d2db8 elementor-widget elementor-widget-text-editor\" data-id=\"67d2db8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2>A quiet shift happening inside IT teams<\/h2><p data-start=\"5985\" data-end=\"6055\">Over the last year, we\u2019ve noticed a subtle shift in CIO conversations.<\/p><p data-start=\"6057\" data-end=\"6086\">Early discussions focused on:<\/p><ul><li data-start=\"6089\" data-end=\"6103\">Model accuracy<\/li><li data-start=\"6106\" data-end=\"6123\">Vendor comparison<\/li><li data-start=\"6126\" data-end=\"6139\">Feature lists<\/li><\/ul><p data-start=\"6141\" data-end=\"6176\">More recent conversations focus on:<\/p><ul><li data-start=\"6179\" data-end=\"6203\">Control without friction<\/li><li data-start=\"6206\" data-end=\"6223\">Adoption patterns<\/li><li data-start=\"6226\" data-end=\"6249\">Organizational learning<\/li><li data-start=\"6252\" data-end=\"6266\">Long-term risk<\/li><\/ul><p data-start=\"6268\" data-end=\"6348\">That shift usually happens after initial excitement fades and real usage begins.<\/p><p data-start=\"6350\" data-end=\"6405\">It\u2019s also where AI strategies either mature or stall.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fbbfd26 elementor-widget elementor-widget-text-editor\" data-id=\"fbbfd26\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-start=\"6412\" data-end=\"6446\">The bridge to the next decision<\/h2><p data-start=\"6448\" data-end=\"6479\">Once organizations accept that:<\/p><ul><li data-start=\"6482\" data-end=\"6521\">Models are necessary but not sufficient<\/li><li data-start=\"6524\" data-end=\"6547\">Context shapes outcomes<\/li><li data-start=\"6550\" data-end=\"6573\">Structure enables trust<\/li><\/ul><p data-start=\"6575\" data-end=\"6608\">A new question naturally follows:<\/p><p data-start=\"6610\" data-end=\"6692\"><a href=\"http:\/\/aicamp.so\/blog\/how-cios-should-evaluate-ai-platforms\/\"><strong data-start=\"6610\" data-end=\"6692\">What should CIOs actually evaluate when choosing an AI platform for employees?<\/strong><\/a><\/p><p data-start=\"6694\" data-end=\"6759\">Not in terms of features but in terms of operational readiness.<\/p><p data-start=\"6761\" data-end=\"6790\">That\u2019s what we\u2019ll cover next.<\/p><p data-start=\"6792\" data-end=\"7003\">The third article in this series will introduce a <strong data-start=\"6842\" data-end=\"6876\">practical evaluation framework<\/strong> CIOs and IT leaders can use to assess AI platforms based on governance, scale, and real-world adoption not marketing claims.<\/p><p data-start=\"7005\" data-end=\"7104\">If AI is becoming part of how your organization works, this is where clarity starts to matter most.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>This series is written for CIOs and IT leaders responsible for AI rollout in growing organizations. After publishing the first article in this series, a few CIOs replied with a variation of the same question. \u201cIf the underlying AI model is the same, why do outcomes differ so much across tools and teams?\u201d It\u2019s a [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":7117,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[35,33],"tags":[],"class_list":["post-7181","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-enterprise","category-founders-corner"],"_links":{"self":[{"href":"https:\/\/aicamp.so\/blog\/wp-json\/wp\/v2\/posts\/7181","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aicamp.so\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aicamp.so\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aicamp.so\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/aicamp.so\/blog\/wp-json\/wp\/v2\/comments?post=7181"}],"version-history":[{"count":3,"href":"https:\/\/aicamp.so\/blog\/wp-json\/wp\/v2\/posts\/7181\/revisions"}],"predecessor-version":[{"id":7239,"href":"https:\/\/aicamp.so\/blog\/wp-json\/wp\/v2\/posts\/7181\/revisions\/7239"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aicamp.so\/blog\/wp-json\/wp\/v2\/media\/7117"}],"wp:attachment":[{"href":"https:\/\/aicamp.so\/blog\/wp-json\/wp\/v2\/media?parent=7181"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aicamp.so\/blog\/wp-json\/wp\/v2\/categories?post=7181"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aicamp.so\/blog\/wp-json\/wp\/v2\/tags?post=7181"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}