{"id":56,"date":"2026-03-25T08:49:23","date_gmt":"2026-03-25T08:49:23","guid":{"rendered":"https:\/\/dracau.com\/blog\/?p=56"},"modified":"2026-03-25T09:40:21","modified_gmt":"2026-03-25T09:40:21","slug":"what-are-llms-a-business-guide-to-large-language-models","status":"publish","type":"post","link":"https:\/\/dracau.com\/blog\/what-are-llms-a-business-guide-to-large-language-models\/","title":{"rendered":"What Are LLMs? A Business Guide to Large Language Models"},"content":{"rendered":"\n<p>Large language models, or LLMs, are AI models trained on massive amounts of text and other data so they can understand patterns in language, generate responses, summarize information, answer questions, translate content, and perform a wide range of natural language tasks. Major platform providers describe LLMs as statistical or deep learning models, commonly based on transformer architectures, that can be adapted for tasks like generation, summarization, classification, translation, and conversational assistance.<\/p>\n\n\n\n<p>For business leaders, that definition is only the starting point.<\/p>\n\n\n\n<p>The real question is not just what LLMs are, but what they actually mean for operations, workflows, and decision-making. In practice, LLMs are becoming a flexible business layer for working with language-heavy tasks: documents, support tickets, proposals, research, internal knowledge, CRM notes, reports, and communication across teams. Enterprise vendors increasingly position LLMs as a foundation for chatbots, search, summarization, document processing, and workflow assistance rather than as standalone novelty tools.<\/p>\n\n\n\n<p>This guide explains what LLMs are, how they work in plain English, where they create business value, where they fail, and how companies should think about implementing them responsibly.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What LLMs actually are<\/h2>\n\n\n\n<p>An LLM is a model trained to predict and generate language by learning patterns from very large datasets. Because of that training, it can produce human-like text, follow instructions, summarize long inputs, classify content, answer questions, and often work across many tasks without being trained separately for each one. Google Cloud describes LLMs as models trained on massive amounts of data that can generate and translate text and perform other natural language processing tasks, while <a href=\"https:\/\/www.nvidia.com\/en-eu\/glossary\/large-language-models\/\">NVIDIA<\/a> describes them as deep learning algorithms that can recognize, summarize, translate, predict, and generate content.<\/p>\n\n\n\n<p>A simple way to understand it:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>traditional software follows fixed rules<\/li>\n\n\n\n<li>an LLM predicts useful language based on patterns it learned during training<\/li>\n<\/ul>\n\n\n\n<p>That is why LLMs feel flexible. They are not hard-coded for one script. They can respond to many kinds of requests using the same underlying model.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why they are called \u201clarge language models\u201d<\/h2>\n\n\n\n<p>They are called \u201clarge\u201d for two reasons.<\/p>\n\n\n\n<p>First, they are trained on very large datasets. Second, they usually contain extremely large numbers of model parameters and layers, which help them represent complex relationships in language. Google and NVIDIA both describe LLMs as being trained on massive or internet-scale corpora, typically using transformer-based deep learning systems.<\/p>\n\n\n\n<p>They are called \u201clanguage models\u201d because their core function is modeling language: predicting what text is likely to come next, what a passage means, how a question should be answered, or how a response should be formatted.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How LLMs work in simple terms<\/h2>\n\n\n\n<p>At a high level, an LLM is trained to identify patterns and relationships across huge amounts of language data. Modern LLMs are typically built on transformer architectures, which are designed to understand context and relationships between words or tokens more effectively than older sequence-processing approaches. <a href=\"https:\/\/cloud.google.com\/ai\/llms\">Google Cloud<\/a> explicitly notes that modern LLMs are typically based on transformer architectures, and NVIDIA describes transformers as the underlying architecture behind many successful LLMs.<\/p>\n\n\n\n<p>In plain English, that means the model learns things like:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>how concepts relate to each other<\/li>\n\n\n\n<li>how language is structured<\/li>\n\n\n\n<li>what kinds of answers fit what kinds of prompts<\/li>\n\n\n\n<li>how tone, format, and intent affect output<\/li>\n<\/ul>\n\n\n\n<p>When you give it a prompt, the model does not \u201clook up\u201d a fixed answer the way a database does. It generates a response based on probabilities shaped by its training and any additional context you provide at runtime.<\/p>\n\n\n\n<p>That is also why the same model can be used for many business tasks, from summarization and classification to drafting, retrieval support, and chatbot interactions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">LLMs are not the same as chatbots<\/h2>\n\n\n\n<p>This distinction matters.<\/p>\n\n\n\n<p>A chatbot is an application or interface.<\/p>\n\n\n\n<p>An LLM is the model that can power part of that application.<\/p>\n\n\n\n<p>A chatbot may use:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>an LLM<\/li>\n\n\n\n<li>retrieval from internal documents<\/li>\n\n\n\n<li>workflows and business rules<\/li>\n\n\n\n<li>user authentication<\/li>\n\n\n\n<li>escalation logic<\/li>\n\n\n\n<li>API calls to business systems<\/li>\n<\/ul>\n\n\n\n<p>In other words, the chatbot is the product experience. The LLM is one of the engines behind it.<\/p>\n\n\n\n<p>This is important because many businesses think \u201cwe need an LLM\u201d when what they actually need is a workflow, an AI assistant, an internal knowledge system, or a controlled integration layer. Google Cloud\u2019s enterprise material reflects this by positioning LLMs as building blocks inside broader solutions like agents, enterprise search, document summarization, and contact center systems.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What LLMs are good at in business<\/h2>\n\n\n\n<p>LLMs are strongest when work is language-heavy, repetitive, and context-dependent.<\/p>\n\n\n\n<p>Common strengths include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>summarizing long documents<\/li>\n\n\n\n<li>extracting key points from messy text<\/li>\n\n\n\n<li>answering natural-language questions<\/li>\n\n\n\n<li>classifying content<\/li>\n\n\n\n<li>drafting emails, reports, and proposals<\/li>\n\n\n\n<li>rewriting content in a clearer structure<\/li>\n\n\n\n<li>translating or standardizing language<\/li>\n\n\n\n<li>helping users search internal knowledge faster<\/li>\n<\/ul>\n\n\n\n<p>These use cases are consistent across major enterprise AI platforms, which highlight summarization, research support, chatbots, search, translation, and content generation as common LLM applications.<\/p>\n\n\n\n<p>For businesses, the key advantage is leverage: one model can support multiple language workflows instead of requiring a separate system for every text-based task.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What LLMs are not good at<\/h2>\n\n\n\n<p>LLMs are powerful, but they are not automatically reliable.<\/p>\n\n\n\n<p>They can produce incorrect answers, fabricate details, overstate confidence, miss context, or handle sensitive workflows poorly if they are deployed without controls. NIST\u2019s AI Risk Management Framework and its Generative AI Profile both emphasize that organizations need structured approaches to trustworthiness, governance, evaluation, transparency, safety, privacy, and risk management when developing or using AI systems.<\/p>\n\n\n\n<p>That means LLMs are usually a bad fit when:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>outputs must be perfectly accurate without review<\/li>\n\n\n\n<li>the task is better solved by rules or databases<\/li>\n\n\n\n<li>permissions and data handling are not defined<\/li>\n\n\n\n<li>there is no human fallback for high-risk actions<\/li>\n\n\n\n<li>the business expects \u201cautonomous intelligence\u201d with no system design<\/li>\n<\/ul>\n\n\n\n<p>In business, the fastest way to fail with LLMs is to treat them like magic instead of infrastructure.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The main business use cases for LLMs<\/h2>\n\n\n\n<h2 class=\"wp-block-heading\">Internal knowledge and document search<\/h2>\n\n\n\n<p>One of the highest-value uses of LLMs is helping employees find and understand information faster.<\/p>\n\n\n\n<p>Examples:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>searching policy libraries<\/li>\n\n\n\n<li>summarizing proposals<\/li>\n\n\n\n<li>extracting answers from internal docs<\/li>\n\n\n\n<li>comparing multiple documents<\/li>\n\n\n\n<li>briefing teams before meetings<\/li>\n<\/ul>\n\n\n\n<p>Google Cloud explicitly highlights research and information discovery, enterprise search, and document summarization as core LLM use cases.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Customer support and service operations<\/h2>\n\n\n\n<p>LLMs can help:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>draft support replies<\/li>\n\n\n\n<li>classify tickets<\/li>\n\n\n\n<li>summarize conversations<\/li>\n\n\n\n<li>retrieve relevant help content<\/li>\n\n\n\n<li>assist human agents in real time<\/li>\n<\/ul>\n\n\n\n<p>Used correctly, this reduces response time and improves operational consistency. Used badly, it creates wrong answers at scale.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Sales and revenue workflows<\/h2>\n\n\n\n<p>LLMs are useful for:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>call note summarization<\/li>\n\n\n\n<li>CRM text cleanup<\/li>\n\n\n\n<li>proposal drafting<\/li>\n\n\n\n<li>lead qualification support<\/li>\n\n\n\n<li>personalized outreach drafts<\/li>\n\n\n\n<li>account research summaries<\/li>\n<\/ul>\n\n\n\n<p>The core benefit is not \u201cautomated selling.\u201d It is reducing repetitive language work around the sales process.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Marketing and content operations<\/h2>\n\n\n\n<p>Common applications include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>content ideation<\/li>\n\n\n\n<li>brief generation<\/li>\n\n\n\n<li>repurposing long-form content<\/li>\n\n\n\n<li>summarizing research<\/li>\n\n\n\n<li>writing first drafts<\/li>\n\n\n\n<li>organizing customer feedback themes<\/li>\n<\/ul>\n\n\n\n<p>This is valuable, but only if editorial standards and review remain in place.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Compliance, operations, and admin workflows<\/h2>\n\n\n\n<p>LLMs can support:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>policy interpretation assistance<\/li>\n\n\n\n<li>contract summarization<\/li>\n\n\n\n<li>process documentation<\/li>\n\n\n\n<li>internal FAQ systems<\/li>\n\n\n\n<li>status reporting<\/li>\n\n\n\n<li>meeting and project summaries<\/li>\n<\/ul>\n\n\n\n<p>This is often where businesses see immediate time savings because so much internal work depends on reading, writing, and structuring information.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">LLMs vs traditional automation<\/h2>\n\n\n\n<p>Traditional automation follows predefined logic.<\/p>\n\n\n\n<p>LLMs add flexibility where the input is messy or language-based.<\/p>\n\n\n\n<p>For example:<\/p>\n\n\n\n<p>Traditional automation:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>if subject line contains \u201cinvoice,\u201d send to finance<\/li>\n<\/ul>\n\n\n\n<p>LLM-enabled workflow:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>read the full message, determine intent, extract the key request, summarize it, and route it to the correct team with context<\/li>\n<\/ul>\n\n\n\n<p>That does not mean LLMs replace automation. It means they improve automation in workflows where human language creates ambiguity.<\/p>\n\n\n\n<p>The strongest business systems usually combine both:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>rules for control<\/li>\n\n\n\n<li>LLMs for interpretation<\/li>\n\n\n\n<li>integrations for action<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">LLMs vs AI agents<\/h2>\n\n\n\n<p>LLMs and <a href=\"https:\/\/dracau.com\/blog\/what-are-ai-agents-types-use-cases-and-architecture\/\">AI agents<\/a> are related, but they are not the same thing.<\/p>\n\n\n\n<p>An LLM is the reasoning or language engine.<\/p>\n\n\n\n<p>An AI agent is a broader system that can use an LLM alongside tools, memory, policies, workflows, and action logic to complete tasks.<\/p>\n\n\n\n<p>So when a company says it wants an \u201cAI agent,\u201d it usually needs more than just an LLM. It needs:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>the model<\/li>\n\n\n\n<li>context and data access<\/li>\n\n\n\n<li>orchestration logic<\/li>\n\n\n\n<li>tool integrations<\/li>\n\n\n\n<li>permissions<\/li>\n\n\n\n<li>guardrails<\/li>\n\n\n\n<li>monitoring<\/li>\n<\/ul>\n\n\n\n<p>That is why many real business projects are actually integration projects, not just \u201cmodel projects.\u201d<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What businesses need in addition to an LLM<\/h2>\n\n\n\n<p>An LLM alone is rarely enough.<\/p>\n\n\n\n<p>To create real business value, companies usually need an LLM connected to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>internal documents<\/li>\n\n\n\n<li>structured business systems<\/li>\n\n\n\n<li>APIs<\/li>\n\n\n\n<li>workflow platforms<\/li>\n\n\n\n<li>approval steps<\/li>\n\n\n\n<li>analytics<\/li>\n\n\n\n<li>security controls<\/li>\n<\/ul>\n\n\n\n<p>This is why enterprise platforms emphasize building applications around LLMs, not simply exposing the model in isolation. Google Cloud highlights document pipelines, enterprise search, agents, and contact center systems. OpenAI highlights enterprise security, privacy, connectors, and data integration as part of ChatGPT Enterprise.<\/p>\n\n\n\n<p>In practical terms, this means the business question should usually be:<\/p>\n\n\n\n<p>Not: \u201cWhich LLM should we use?\u201d<br>But: \u201cWhich workflow should we improve, and what model-plus-integration setup supports that safely?\u201d<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The biggest risks businesses should understand<\/h2>\n\n\n\n<p>LLMs can create real productivity gains, but they also introduce real risk.<\/p>\n\n\n\n<p>NIST\u2019s AI Risk Management Framework and Generative AI Profile are useful here because they frame AI use around trustworthiness, safety, security, transparency, privacy, fairness, and accountability rather than just capability.<\/p>\n\n\n\n<p>The main practical risks include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>hallucinated or false outputs<\/li>\n\n\n\n<li>data leakage through poor handling of sensitive information<\/li>\n\n\n\n<li>overreliance by employees<\/li>\n\n\n\n<li>poor traceability of outputs<\/li>\n\n\n\n<li>inconsistent performance across tasks<\/li>\n\n\n\n<li>lack of governance or approval controls<\/li>\n\n\n\n<li>unclear ownership when something goes wrong<\/li>\n<\/ul>\n\n\n\n<p>For most businesses, the answer is not avoiding LLMs completely. It is deploying them with boundaries.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How businesses should evaluate LLM opportunities<\/h2>\n\n\n\n<p>A good business LLM use case usually has these traits:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>high volume<\/li>\n\n\n\n<li>repetitive language work<\/li>\n\n\n\n<li>clear business value<\/li>\n\n\n\n<li>enough structure to validate outputs<\/li>\n\n\n\n<li>low or manageable risk<\/li>\n\n\n\n<li>measurable time savings or quality improvement<\/li>\n<\/ul>\n\n\n\n<p>Good early use cases:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>summarizing documents<\/li>\n\n\n\n<li>drafting internal reports<\/li>\n\n\n\n<li>classifying inbound requests<\/li>\n\n\n\n<li>searching internal knowledge<\/li>\n\n\n\n<li>helping staff retrieve answers faster<\/li>\n<\/ul>\n\n\n\n<p>Poor early use cases:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>autonomous legal decisions<\/li>\n\n\n\n<li>unsupervised financial approvals<\/li>\n\n\n\n<li>customer-facing high-risk outputs without review<\/li>\n\n\n\n<li>complex multi-system workflows with no monitoring<\/li>\n<\/ul>\n\n\n\n<p>The goal is not to start with the most advanced use case. The goal is to start with the safest useful one.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What implementation usually looks like<\/h2>\n\n\n\n<p>A practical LLM rollout often follows this path:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 1: choose one business workflow<\/h3>\n\n\n\n<p>Pick a real workflow, not a vague ambition.<\/p>\n\n\n\n<p>Example:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>internal policy search<\/li>\n\n\n\n<li>sales call summarization<\/li>\n\n\n\n<li>support ticket triage<\/li>\n\n\n\n<li>proposal drafting<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Step 2: define success clearly<\/h3>\n\n\n\n<p>Measure outcomes like:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>time saved<\/li>\n\n\n\n<li>response speed<\/li>\n\n\n\n<li>output quality<\/li>\n\n\n\n<li>reduced manual effort<\/li>\n\n\n\n<li>fewer repetitive tasks<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Step 3: connect the model to the right data<\/h3>\n\n\n\n<p>Most business value comes from grounding the system in actual company context, not just using the base model with generic prompts.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 4: add control layers<\/h3>\n\n\n\n<p>Use:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>permission rules<\/li>\n\n\n\n<li>human review<\/li>\n\n\n\n<li>audit trails<\/li>\n\n\n\n<li>prompt controls<\/li>\n\n\n\n<li>response validation<\/li>\n\n\n\n<li>fallback logic<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Step 5: improve over time<\/h3>\n\n\n\n<p>LLM systems work best when they are treated as products that need testing, feedback, and refinement.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">So what are LLMs, really?<\/h2>\n\n\n\n<p>For businesses, the most useful answer is this:<\/p>\n\n\n\n<p><strong>LLMs are flexible AI models that help systems understand and generate language, making them useful for document-heavy, communication-heavy, and knowledge-heavy workflows.<\/strong><\/p>\n\n\n\n<p>That is their real business meaning.<\/p>\n\n\n\n<p>They are not just chat tools. They are not just content generators. They are a new interface layer for working with language across operations, support, research, sales, and internal systems.<\/p>\n\n\n\n<p>The value comes when they are connected to the right workflows.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Final takeaway<\/h2>\n\n\n\n<p>LLMs matter because so much business work is language work.<\/p>\n\n\n\n<p>Teams read documents. Write updates. Summarize calls. Search internal knowledge. Draft proposals. Answer questions. Classify requests. Interpret messy inputs.<\/p>\n\n\n\n<p>Large language models make those workflows more scalable.<\/p>\n\n\n\n<p>But business value does not come from using an LLM by itself. It comes from integrating the model into a controlled system that fits your operations, data, and goals. Enterprise guidance from Google Cloud, NVIDIA, OpenAI, and NIST all points in the same direction: LLMs are powerful, but successful deployment depends on the surrounding architecture, controls, data access, and risk management.<\/p>\n\n\n\n<p>If your business is exploring how LLMs fit into internal workflows, knowledge systems, or operational automation, the next step is not just choosing a model. It is designing the right integration layer around it you can contact us at <a href=\"https:\/\/dracau.com\/services\/ai-automation\/ai-integration\/\">AI Integration Services<\/a> on Dracau.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">FAQ<\/h2>\n\n\n\n<div data-schema-only=\"false\" class=\"wp-block-aioseo-faq\"><h3 class=\"aioseo-faq-block-question\">What is an LLM in simple terms?<\/h3><div class=\"aioseo-faq-block-answer\">\n<p>An LLM is an AI model trained on very large datasets so it can understand and generate language. Businesses use LLMs for tasks like summarization, question answering, drafting, classification, and internal knowledge assistance.<\/p>\n<\/div><\/div>\n\n\n\n<div data-schema-only=\"false\" class=\"wp-block-aioseo-faq\"><h3 class=\"aioseo-faq-block-question\">What do large language models do in business?<\/h3><div class=\"aioseo-faq-block-answer\">\n<p>Large language models help businesses handle language-heavy work more efficiently. Common use cases include document summarization, support assistance, knowledge search, sales note summaries, content drafting, and workflow support.<\/p>\n<\/div><\/div>\n\n\n\n<div data-schema-only=\"false\" class=\"wp-block-aioseo-faq\"><h3 class=\"aioseo-faq-block-question\">Are LLMs the same as chatbots?<\/h3><div class=\"aioseo-faq-block-answer\">\n<p>No. An LLM is the underlying model, while a chatbot is an application or interface that may use an LLM along with workflows, retrieval systems, integrations, and business logic.<\/p>\n<\/div><\/div>\n\n\n\n<div data-schema-only=\"false\" class=\"wp-block-aioseo-faq\"><h3 class=\"aioseo-faq-block-question\">What are the risks of using LLMs in business?<\/h3><div class=\"aioseo-faq-block-answer\">\n<p>The main risks include inaccurate outputs, weak governance, privacy problems, overreliance, and lack of transparency or accountability. NIST recommends structured AI risk management to address these issues.<\/p>\n<\/div><\/div>\n\n\n\n<div data-schema-only=\"false\" class=\"wp-block-aioseo-faq\"><h3 class=\"aioseo-faq-block-question\">Do businesses need more than just an LLM?<\/h3><div class=\"aioseo-faq-block-answer\">\n<p>Yes. Most businesses need an LLM connected to internal data, workflows, permissions, and monitoring systems. Real value usually comes from integration, not from the model alone.<\/p>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Large language models, or LLMs, are AI models trained on massive amounts of text and other data so they can understand patterns in language, generate responses,&#8230;<\/p>\n","protected":false},"author":1,"featured_media":57,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[7],"tags":[58,21,53,56,57,52,55,59,51,54],"class_list":["post-56","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-integration","tag-ai-integration","tag-business-ai-automation","tag-enterprise-ai","tag-generative-ai-for-business","tag-language-models","tag-large-language-models","tag-llm-business-guide","tag-llm-use-cases","tag-llms-for-business","tag-what-are-llms"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/dracau.com\/blog\/wp-json\/wp\/v2\/posts\/56","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dracau.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dracau.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dracau.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/dracau.com\/blog\/wp-json\/wp\/v2\/comments?post=56"}],"version-history":[{"count":1,"href":"https:\/\/dracau.com\/blog\/wp-json\/wp\/v2\/posts\/56\/revisions"}],"predecessor-version":[{"id":58,"href":"https:\/\/dracau.com\/blog\/wp-json\/wp\/v2\/posts\/56\/revisions\/58"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dracau.com\/blog\/wp-json\/wp\/v2\/media\/57"}],"wp:attachment":[{"href":"https:\/\/dracau.com\/blog\/wp-json\/wp\/v2\/media?parent=56"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dracau.com\/blog\/wp-json\/wp\/v2\/categories?post=56"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dracau.com\/blog\/wp-json\/wp\/v2\/tags?post=56"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}