{"id":5517,"date":"2026-09-26T03:53:33","date_gmt":"2026-09-26T06:53:33","guid":{"rendered":"https:\/\/tucumandevelopers.com\/index.php\/2026\/09\/26\/rag-explained-a-beginners-guide-to-retrieval-augmented-generation\/"},"modified":"2026-09-26T03:53:33","modified_gmt":"2026-09-26T06:53:33","slug":"rag-explained-a-beginners-guide-to-retrieval-augmented-generation","status":"publish","type":"post","link":"https:\/\/tucumandevelopers.com\/index.php\/2026\/09\/26\/rag-explained-a-beginners-guide-to-retrieval-augmented-generation\/","title":{"rendered":"RAG Explained: A Beginner&#8217;s Guide to Retrieval-Augmented Generation"},"content":{"rendered":"<div>\n<div><\/div>\n<p>Let&#8217;s see how this works.<\/p>\n<hr>\n<h2> <a name=\"a-simple-realworld-example\" href=\"#a-simple-realworld-example\"> <\/a> \ud83c\udfe8 A Simple Real-World Example <\/h2>\n<p>Suppose your company&#8217;s travel policy says:<\/p>\n<blockquote>\n<p>Employees can claim hotel expenses up to \u20b95,000 per night when traveling for business.<\/p>\n<\/blockquote>\n<p>An employee asks:<\/p>\n<blockquote>\n<p><strong>&#8220;What is the hotel reimbursement limit?&#8221;<\/strong><\/p>\n<\/blockquote>\n<p>A RAG application can:<\/p>\n<ol>\n<li>Search the company&#8217;s travel policy.<\/li>\n<li>Find the relevant information.<\/li>\n<li>Provide that information to the AI.<\/li>\n<li>Generate an answer.<\/li>\n<\/ol>\n<p>The AI can then respond:<\/p>\n<blockquote>\n<p><strong>&#8220;According to the travel policy, employees can claim hotel expenses up to \u20b95,000 per night.&#8221;<\/strong><\/p>\n<\/blockquote>\n<p>Notice what happened?<\/p>\n<p>The AI didn&#8217;t need to already know your company&#8217;s travel policy.<\/p>\n<p>The application <strong>retrieved the relevant information first<\/strong>.<\/p>\n<p>That&#8217;s the basic idea behind RAG.<\/p>\n<hr>\n<h2> <a name=\"how-does-rag-actually-work\" href=\"#how-does-rag-actually-work\"> <\/a> \ud83d\udd0d How Does RAG Actually Work? <\/h2>\n<p>Let&#8217;s break it down into a few simple steps.<\/p>\n<p>Don&#8217;t worry \u2014 we&#8217;ll keep the technical jargon to a minimum.<\/p>\n<p>A basic RAG application can be understood in <strong>two stages<\/strong>:<\/p>\n<ol>\n<li><strong>Prepare the knowledge<\/strong><\/li>\n<li><strong>Answer the user&#8217;s question<\/strong><\/li>\n<\/ol>\n<p>Let&#8217;s look at both.<\/p>\n<hr>\n<h2> <a name=\"stage-1-prepare-the-knowledge\" href=\"#stage-1-prepare-the-knowledge\"> <\/a> \ud83d\udcda Stage 1: Prepare the Knowledge <\/h2>\n<p>Before users can ask questions, we need to prepare the documents so that relevant information can be found quickly.<\/p>\n<h3> <a name=\"step-1-collect-your-documents\" href=\"#step-1-collect-your-documents\"> <\/a> \ud83d\udcc4 Step 1: Collect Your Documents <\/h3>\n<p>Your knowledge could come from many places:<\/p>\n<ul>\n<li>\ud83d\udcc4 PDF files<\/li>\n<li>\ud83d\udcdd Word documents<\/li>\n<li>\ud83c\udf10 Websites<\/li>\n<li>\ud83d\uddc4\ufe0f Databases<\/li>\n<li>\u2601\ufe0f Cloud storage<\/li>\n<li>\ud83d\udcda Knowledge bases<\/li>\n<\/ul>\n<p>For example: <\/p>\n<div>\n<pre><code>Company Knowledge \u2502 \u251c\u2500\u2500 HR Policy.pdf \u251c\u2500\u2500 Travel Policy.pdf \u251c\u2500\u2500 Security Policy.pdf \u2514\u2500\u2500 Product Manual.pdf <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>These documents become the <strong>knowledge source<\/strong> for our AI application.<\/p>\n<hr>\n<h3> <a name=\"step-2-break-large-documents-into-smaller-pieces\" href=\"#step-2-break-large-documents-into-smaller-pieces\"> <\/a> \u2702\ufe0f Step 2: Break Large Documents into Smaller Pieces <\/h3>\n<p>Imagine you have a <strong>100-page travel policy<\/strong>.<\/p>\n<p>We don&#8217;t want to send all 100 pages to the AI every time someone asks a question.<\/p>\n<p>Instead, we divide the document into smaller pieces called <strong>chunks<\/strong>. <\/p>\n<div>\n<pre><code>Travel Policy \u2502 \u251c\u2500\u2500 Chunk 1 \u251c\u2500\u2500 Chunk 2 \u251c\u2500\u2500 Chunk 3 \u251c\u2500\u2500 Chunk 4 \u2514\u2500\u2500 ... <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>Why?<\/p>\n<p>Because when someone asks a question, we usually need only the <strong>small section related to that question<\/strong>.<\/p>\n<p>For example:<\/p>\n<blockquote>\n<div>\n<p><strong>Question:<\/strong><\/p>\n<p> &#8220;What is the hotel reimbursement limit?&#8221;<\/p>\n<\/div>\n<\/blockquote>\n<p>The system might retrieve: <\/p>\n<div>\n<pre><code>Chunk 27: \"Employees can claim hotel expenses up to \u20b95,000 per night...\" <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>That&#8217;s much more useful than sending the entire document to the AI.<\/p>\n<h3> <a name=\"think-of-it-this-way\" href=\"#think-of-it-this-way\"> <\/a> \ud83d\udca1 Think of it this way <\/h3>\n<p>Imagine giving a student a 500-page textbook and asking:<\/p>\n<blockquote>\n<p>&#8220;Find the paragraph about hotel reimbursement.&#8221;<\/p>\n<\/blockquote>\n<p>It would take time to search through the entire book.<\/p>\n<p>Instead, if we already know the relevant page, we can go directly there.<\/p>\n<p><strong>Chunking helps us create those smaller searchable pieces of information.<\/strong><\/p>\n<hr>\n<h3> <a name=\"step-3-create-embeddings\" href=\"#step-3-create-embeddings\"> <\/a> \ud83e\udde0 Step 3: Create Embeddings <\/h3>\n<p>Here&#8217;s our first technical concept:<\/p>\n<p><strong>Embeddings.<\/strong><\/p>\n<p>Embeddings are numerical representations of text that help systems measure semantic similarity.<\/p>\n<blockquote>\n<p><strong>Embeddings help a system find information based on semantic similarity, rather than only matching exact words.<\/strong><\/p>\n<\/blockquote>\n<p>For example:<\/p>\n<p><strong>User asks:<\/strong><\/p>\n<blockquote>\n<p>&#8220;How much can I claim for a hotel?&#8221;<\/p>\n<\/blockquote>\n<p>The document says:<\/p>\n<blockquote>\n<p>&#8220;Hotel accommodation expenses are limited to \u20b95,000 per night.&#8221;<\/p>\n<\/blockquote>\n<p>The words aren&#8217;t exactly the same.<\/p>\n<p>But the <strong>meaning is related<\/strong>.<\/p>\n<p>Embeddings help the system identify this semantic similarity.<\/p>\n<p>You can think of it like this: <\/p>\n<div>\n<pre><code>Text \u2193 Embedding Model \u2193 Numerical Vector \u2193 Compare with other vectors \u2193 Find semantically similar content <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>You don&#8217;t need to understand the mathematics behind embeddings to start learning RAG.<\/p>\n<p>The important thing to remember is:<\/p>\n<blockquote>\n<p><strong>Embeddings help a system find information based on semantic similarity, rather than only matching exact words.<\/strong><\/p>\n<\/blockquote>\n<hr>\n<h3> <a name=\"step-4-store-the-information\" href=\"#step-4-store-the-information\"> <\/a> \ud83d\uddc2\ufe0f Step 4: Store the Information <\/h3>\n<p>Now we need somewhere to store our document chunks and their embeddings.<\/p>\n<p>This is where <strong>vector databases and vector search systems<\/strong> come in.<\/p>\n<p>Some popular options include:<\/p>\n<ul>\n<li>Azure AI Search<\/li>\n<li>PostgreSQL + pgvector<\/li>\n<li>Pinecone<\/li>\n<li>Qdrant<\/li>\n<li>Weaviate<\/li>\n<\/ul>\n<p>Think of this as a <strong>smart search system for your documents<\/strong>.<\/p>\n<p>Conceptually: <\/p>\n<div>\n<pre><code>Document Chunk + Embedding + Metadata \u2193 Vector Search <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>Metadata can contain information such as: <\/p>\n<div>\n<pre><code>Document: TravelPolicy.pdf Department: Finance Year: 2026 DocumentType: Policy <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>This additional information can help the application filter and organize the data.<\/p>\n<hr>\n<h2> <a name=\"stage-2-answer-the-users-question\" href=\"#stage-2-answer-the-users-question\"> <\/a> \ud83d\udd0e Stage 2: Answer the User&#8217;s Question <\/h2>\n<p>Now our knowledge has been prepared.<\/p>\n<p>Let&#8217;s see what happens when a user asks a question.<\/p>\n<h3> <a name=\"step-5-the-user-asks-a-question\" href=\"#step-5-the-user-asks-a-question\"> <\/a> \ud83d\udd0e Step 5: The User Asks a Question <\/h3>\n<p>The employee asks:<\/p>\n<blockquote>\n<p><strong>&#8220;What is the hotel reimbursement limit?&#8221;<\/strong><\/p>\n<\/blockquote>\n<p>The application converts the question into an embedding and uses it to search the knowledge base.<\/p>\n<p>The search might return several results: <\/p>\n<div>\n<pre><code>Result 1: Employees can claim hotel expenses up to \u20b95,000 per night. Result 2: Business travel expenses must be submitted within 30 days. Result 3: International travel requires manager approval. <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>The application identifies the information that is <strong>most relevant to the question<\/strong>.<\/p>\n<hr>\n<h3> <a name=\"step-6-give-the-information-to-the-ai\" href=\"#step-6-give-the-information-to-the-ai\"> <\/a> \ud83e\udd16 Step 6: Give the Information to the AI <\/h3>\n<p>Now we have two important pieces:<\/p>\n<p><strong>Question:<\/strong><\/p>\n<blockquote>\n<p>What is the hotel reimbursement limit?<\/p>\n<\/blockquote>\n<p><strong>Relevant information:<\/strong><\/p>\n<blockquote>\n<p>Employees can claim hotel expenses up to \u20b95,000 per night.<\/p>\n<\/blockquote>\n<p>The application provides the question and retrieved context to the LLM.<\/p>\n<p>Conceptually: <\/p>\n<div>\n<pre><code>User Question + Retrieved Context \u2193 Prompt \u2193 LLM \u2193 Generated Answer <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>The LLM can then generate:<\/p>\n<blockquote>\n<p><strong>&#8220;Employees can claim hotel expenses up to \u20b95,000 per night.&#8221;<\/strong><\/p>\n<\/blockquote>\n<p>And that&#8217;s the <strong>Generation<\/strong> part of <em>Retrieval-Augmented Generation<\/em>.<\/p>\n<hr>\n<h2> <a name=\"the-complete-rag-flow\" href=\"#the-complete-rag-flow\"> <\/a> \ud83e\udde9 The Complete RAG Flow <\/h2>\n<p>Now let&#8217;s separate the process into two simple flows.<\/p>\n<h3> <a name=\"preparing-the-knowledge\" href=\"#preparing-the-knowledge\"> <\/a> Preparing the knowledge <\/h3>\n<div>\n<pre><code>Documents \u2193 Chunking \u2193 Embeddings \u2193 Vector Search Index <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<h3> <a name=\"answering-a-question\" href=\"#answering-a-question\"> <\/a> Answering a question <\/h3>\n<div>\n<pre><code>User Question \u2193 Question Embedding \u2193 Search the Knowledge Base \u2193 Relevant Chunks \u2193 Question + Retrieved Context \u2193 LLM \u2193 Final Answer <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>This is the heart of a RAG application.<\/p>\n<blockquote>\n<p><strong>Retrieve relevant information \u2192 provide it as context \u2192 generate an answer.<\/strong><\/p>\n<\/blockquote>\n<hr>\n<h2> <a name=\"is-rag-just-search\" href=\"#is-rag-just-search\"> <\/a> \ud83d\udd0d Is RAG Just Search? <\/h2>\n<p>Not exactly.<\/p>\n<p>Traditional search might work like this: <\/p>\n<div>\n<pre><code>Question \u2193 Search \u2193 List of Documents <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>You then need to open the documents and find the answer yourself.<\/p>\n<p>RAG goes one step further: <\/p>\n<div>\n<pre><code>Question \u2193 Search Relevant Information \u2193 Provide Context to AI \u2193 Generate Answer <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>So you can think of RAG as:<\/p>\n<blockquote>\n<p><strong>Search + AI<\/strong><\/p>\n<\/blockquote>\n<p>Traditional search helps you <strong>find information<\/strong>.<\/p>\n<p>A RAG application helps an AI system <strong>retrieve relevant information and generate an answer using that information<\/strong>.<\/p>\n<hr>\n<h2> <a name=\"rag-vs-finetuning\" href=\"#rag-vs-finetuning\"> <\/a> \ud83c\udd9a RAG vs Fine-Tuning <\/h2>\n<p>This is another question beginners often have:<\/p>\n<blockquote>\n<p><strong>&#8220;Why not fine-tune the model instead?&#8221;<\/strong><\/p>\n<\/blockquote>\n<p>RAG and fine-tuning solve different problems.<\/p>\n<div>\n<table>\n<thead>\n<tr>\n<th>RAG<\/th>\n<th>Fine-Tuning<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Retrieves external information at query time<\/td>\n<td>Further trains the model<\/td>\n<\/tr>\n<tr>\n<td>Useful when knowledge changes frequently<\/td>\n<td>Useful for specialized behavior or tasks<\/td>\n<\/tr>\n<tr>\n<td>Documents can be updated independently<\/td>\n<td>Requires a training process<\/td>\n<\/tr>\n<tr>\n<td>Commonly used for knowledge-based applications<\/td>\n<td>Commonly used for specialized model behavior<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>A simple way to remember it:<\/p>\n<blockquote>\n<p><strong>RAG \u2192 Give the AI relevant information.<\/strong><\/p>\n<p><strong>Fine-tuning \u2192 Adapt the AI for a specific behavior or task.<\/strong><\/p>\n<\/blockquote>\n<p>For example, if your company&#8217;s HR policy changes next month, you can update the knowledge source used by a RAG application without retraining the language model.<\/p>\n<hr>\n<h2> <a name=\"where-can-we-use-rag\" href=\"#where-can-we-use-rag\"> <\/a> \ud83c\udf0e Where Can We Use RAG? <\/h2>\n<p>RAG isn&#8217;t limited to company policies.<\/p>\n<p>It can be used in many real-world applications.<\/p>\n<h3> <a name=\"developer-assistant\" href=\"#developer-assistant\"> <\/a> \ud83d\udc68\u200d\ud83d\udcbb Developer Assistant <\/h3>\n<p>A developer asks:<\/p>\n<blockquote>\n<p><strong>&#8220;How does our authentication service work?&#8221;<\/strong><\/p>\n<\/blockquote>\n<p>The RAG application searches the company&#8217;s technical documentation and provides relevant context to the LLM.<\/p>\n<hr>\n<h3> <a name=\"hr-assistant\" href=\"#hr-assistant\"> <\/a> \ud83c\udfe2 HR Assistant <\/h3>\n<p>An employee asks:<\/p>\n<blockquote>\n<p><strong>&#8220;How many vacation days can I carry forward?&#8221;<\/strong><\/p>\n<\/blockquote>\n<p>The application searches HR policies and generates an answer based on the retrieved information.<\/p>\n<hr>\n<h3> <a name=\"customer-support\" href=\"#customer-support\"> <\/a> \ud83d\udee0\ufe0f Customer Support <\/h3>\n<p>A customer asks:<\/p>\n<blockquote>\n<p><strong>&#8220;How do I reset my device?&#8221;<\/strong><\/p>\n<\/blockquote>\n<p>The application can search product documentation and provide step-by-step guidance.<\/p>\n<hr>\n<h3> <a name=\"enterprise-knowledge-assistant\" href=\"#enterprise-knowledge-assistant\"> <\/a> \ud83d\udcda Enterprise Knowledge Assistant <\/h3>\n<p>An employee asks:<\/p>\n<blockquote>\n<p><strong>&#8220;What is the process for raising a purchase request?&#8221;<\/strong><\/p>\n<\/blockquote>\n<p>The application searches company procedures and generates a response.<\/p>\n<p>The common pattern is: <\/p>\n<div>\n<pre><code>Company Knowledge \u2193 RAG \u2193 LLM \u2193 AI Assistant <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<hr>\n<h2> <a name=\"does-rag-make-ai-100-accurate\" href=\"#does-rag-make-ai-100-accurate\"> <\/a> \u26a0\ufe0f Does RAG Make AI 100% Accurate? <\/h2>\n<p><strong>No.<\/strong><\/p>\n<p>This is important to understand.<\/p>\n<p>RAG can provide the AI with relevant information, but it does <strong>not guarantee a perfect answer<\/strong>.<\/p>\n<p>For example, if the retrieval system finds the wrong information, the LLM may generate an incorrect response based on that context.<\/p>\n<p>There can also be problems if:<\/p>\n<ul>\n<li>The source document contains incorrect information.<\/li>\n<li>Important information was not retrieved.<\/li>\n<li>The retrieved context is incomplete.<\/li>\n<li>The prompt doesn&#8217;t clearly guide the model.<\/li>\n<li>The model misunderstands the retrieved context.<\/li>\n<\/ul>\n<p>That&#8217;s why production RAG systems need good:<\/p>\n<ul>\n<li>Document processing<\/li>\n<li>Search and retrieval<\/li>\n<li>Security<\/li>\n<li>Prompt design<\/li>\n<li>Evaluation<\/li>\n<li>Monitoring<\/li>\n<\/ul>\n<p>So don&#8217;t think of RAG as a magic solution.<\/p>\n<p>Instead, think of it as a way to <strong>ground an AI application with relevant external information<\/strong>.<\/p>\n<hr>\n<h2> <a name=\"what-does-rag-look-like-with-net-and-azure\" href=\"#what-does-rag-look-like-with-net-and-azure\"> <\/a> \u2601\ufe0f What Does RAG Look Like with .NET and Azure? <\/h2>\n<p>If you&#8217;re a .NET developer, a simple enterprise RAG architecture could look like this: <\/p>\n<div>\n<pre><code> User \u2193 .NET Web API \u2193 Azure AI Search \u2193 Relevant Chunks \u2193 Prompt + Context \u2193 Azure OpenAI \u2193 AI Response <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>Your .NET application can orchestrate the process:<\/p>\n<ol>\n<li>Receive the user&#8217;s question.<\/li>\n<li>Search for relevant information.<\/li>\n<li>Retrieve the relevant chunks.<\/li>\n<li>Build the prompt with the retrieved context.<\/li>\n<li>Send the request to the AI model.<\/li>\n<li>Return the response.<\/li>\n<\/ol>\n<p>This makes RAG particularly interesting for developers working with <strong>.NET and Azure<\/strong>.<\/p>\n<hr>\n<h2> <a name=\"the-5-things-you-need-to-remember\" href=\"#the-5-things-you-need-to-remember\"> <\/a> \ud83c\udfaf The 5 Things You Need to Remember <\/h2>\n<p>If you&#8217;re completely new to RAG, remember these five concepts:<\/p>\n<ol>\n<li>\n<div>\n<p><strong>Documents<\/strong><\/p>\n<p> Where your knowledge lives.<\/p>\n<\/div>\n<\/li>\n<li>\n<div>\n<p><strong>Chunks<\/strong><\/p>\n<p> Smaller pieces of your documents.<\/p>\n<\/div>\n<\/li>\n<li>\n<div>\n<p><strong>Embeddings<\/strong><\/p>\n<p> Numerical representations that help systems measure semantic similarity.<\/p>\n<\/div>\n<\/li>\n<li>\n<div>\n<p><strong>Retrieval<\/strong><\/p>\n<p> Finding information relevant to the user&#8217;s question.<\/p>\n<\/div>\n<\/li>\n<li>\n<div>\n<p><strong>Generation<\/strong><\/p>\n<p> The LLM uses the retrieved information to generate the answer.<\/p>\n<\/div>\n<\/li>\n<\/ol>\n<p>Put them together: <\/p>\n<div>\n<pre><code>Documents \u2193 Chunks \u2193 Embeddings \u2193 Search \u2193 Relevant Context \u2193 LLM \u2193 Answer <\/code><\/pre>\n<div>\n<\/p><\/div>\n<\/p><\/div>\n<p>That&#8217;s <strong>Retrieval-Augmented Generation<\/strong>.<\/p>\n<h2> <a name=\"your-turn\" href=\"#your-turn\"> <\/a> \ud83d\udcac Your Turn <\/h2>\n<p>Have you tried building a RAG application?<\/p>\n<p>What would you like to build with RAG?<\/p>\n<ul>\n<li>\ud83d\udc68\u200d\ud83d\udcbb Developer assistant<\/li>\n<li>\ud83d\udcc4 Document chatbot<\/li>\n<li>\ud83c\udfe2 Enterprise knowledge assistant<\/li>\n<li>\ud83d\udee0\ufe0f Customer support assistant<\/li>\n<li>\ud83e\udd16 Something else<\/li>\n<\/ul>\n<p><strong>Share your idea in the comments. \ud83d\udc47<\/strong><\/p>\n<hr>\n<h2> <a name=\"key-takeaway\" href=\"#key-takeaway\"> <\/a> \ud83d\udccc Key Takeaway <\/h2>\n<p>If you remember just one sentence from this article, remember this:<\/p>\n<blockquote>\n<p><strong>A RAG application retrieves relevant information from a knowledge source and provides it to an LLM so it can generate a more informed response.<\/strong><\/p>\n<\/blockquote>\n<p>And that&#8217;s the foundation of many modern <strong>Generative AI applications<\/strong>.<\/p>\n<\/p><\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>Fuente: <a href=\"https:\/\/dev.to\/chethan_ramaswamy_773955e\/rag-explained-a-beginners-guide-to-retrieval-augmented-generation-2dn9\">Art\u00edculo original<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Let&#8217;s see how this works. \ud83c\udfe8 A Simple Real-World Example Suppose your company&#8217;s travel policy says: Employees can claim hotel expenses up to \u20b95,000 per night when traveling for business. An employee asks: &#8220;What is the hotel reimbursement limit?&#8221; A RAG application can: Search the company&#8217;s travel policy. Find the relevant information. Provide that information [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":5516,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"webixso_pending_account_ids":""},"categories":[41],"tags":[],"class_list":["post-5517","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-devto"],"jetpack_publicize_connections":[],"_links":{"self":[{"href":"https:\/\/tucumandevelopers.com\/index.php\/wp-json\/wp\/v2\/posts\/5517","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tucumandevelopers.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tucumandevelopers.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tucumandevelopers.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/tucumandevelopers.com\/index.php\/wp-json\/wp\/v2\/comments?post=5517"}],"version-history":[{"count":0,"href":"https:\/\/tucumandevelopers.com\/index.php\/wp-json\/wp\/v2\/posts\/5517\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/tucumandevelopers.com\/index.php\/wp-json\/wp\/v2\/media\/5516"}],"wp:attachment":[{"href":"https:\/\/tucumandevelopers.com\/index.php\/wp-json\/wp\/v2\/media?parent=5517"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tucumandevelopers.com\/index.php\/wp-json\/wp\/v2\/categories?post=5517"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tucumandevelopers.com\/index.php\/wp-json\/wp\/v2\/tags?post=5517"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}