{"id":3589,"date":"2025-07-03T10:46:56","date_gmt":"2025-07-03T10:46:56","guid":{"rendered":"https:\/\/promptbestie.com\/?p=3589"},"modified":"2025-07-06T17:44:12","modified_gmt":"2025-07-06T17:44:12","slug":"zero-to-prompt-engineer-complete-career-guide-2024","status":"publish","type":"post","link":"https:\/\/promptbestie.com\/es\/zero-to-prompt-engineer-complete-career-guide-2024\/","title":{"rendered":"From 0 to Prompt Engineer: The Complete Journey to Mastering AI Communication"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Introduction: The Rise of AI&#8217;s Most In-Demand Skill<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Imagine having a conversation with the world&#8217;s most knowledgeable assistant, one that can write code, analyze complex data, create compelling content, and solve intricate problems. The quality of that conversation\u2014and the results you achieve\u2014depends entirely on how well you communicate your needs. This is the essence of prompt engineering, and it&#8217;s rapidly becoming one of the most valuable skills in the AI era.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Prompt engineering isn&#8217;t just about asking ChatGPT questions. It&#8217;s a sophisticated discipline that combines psychology, linguistics, computer science, and domain expertise to effectively communicate with large language models (LLMs). As organizations increasingly integrate AI into their workflows, skilled prompt engineers are commanding salaries ranging from $175,000 to $335,000 annually, according to recent industry reports.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Whether you&#8217;re a software developer looking to enhance your AI capabilities, a researcher seeking to leverage LLMs for complex analysis, or a complete newcomer curious about this emerging field, this comprehensive guide will take you from zero to prompt engineer. We&#8217;ll explore the technical foundations, practical methodologies, essential tools, and career pathways that define modern prompt engineering.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What Is Prompt Engineering and Why Does It Matter?<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Defining Prompt Engineering<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Prompt engineering is the systematic practice of designing, optimizing, and implementing inputs (prompts) to guide artificial intelligence models toward producing desired outputs. It encompasses understanding model behavior, crafting precise instructions, and iteratively refining communication strategies to achieve specific goals.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Unlike traditional programming, which involves writing explicit code instructions, prompt engineering operates in the realm of natural language communication with intelligent systems. However, this communication requires the same level of precision, testing, and optimization as conventional software development.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The Business Impact<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Organizations implementing effective prompt engineering strategies report:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>40-60% improvement<\/strong> in AI output quality and relevance<\/li>\n\n\n\n<li><strong>Reduced operational costs<\/strong> through more efficient AI utilization<\/li>\n\n\n\n<li><strong>Faster time-to-value<\/strong> for AI implementations<\/li>\n\n\n\n<li><strong>Enhanced creative and analytical capabilities<\/strong> across teams<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Major companies like Google, Microsoft, OpenAI, and Anthropic now employ dedicated prompt engineering teams, recognizing that the quality of human-AI interaction directly impacts business outcomes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Core Competencies of Modern Prompt Engineers<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Successful prompt engineers master several interconnected skill areas:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Technical Foundation<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Understanding of transformer architectures and attention mechanisms<\/li>\n\n\n\n<li>Knowledge of tokenization and context windows<\/li>\n\n\n\n<li>Familiarity with various LLM capabilities and limitations<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Communication Excellence<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Precise language crafting and ambiguity reduction<\/li>\n\n\n\n<li>Contextual awareness and instruction clarity<\/li>\n\n\n\n<li>Iterative refinement methodologies<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Domain Expertise<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Deep understanding of specific industry or application areas<\/li>\n\n\n\n<li>Ability to translate domain knowledge into effective prompts<\/li>\n\n\n\n<li>Recognition of domain-specific edge cases and requirements<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">The Technical Foundations Every Prompt Engineer Must Know<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Understanding Large Language Models<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Before crafting effective prompts, you must understand how LLMs process and generate text. Modern models like GPT-4, Claude, and Gemini operate on transformer architectures that use attention mechanisms to understand relationships between words and concepts.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Key Technical Concepts:<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Tokenization:<\/strong> LLMs don&#8217;t process words directly but break text into tokens. Understanding tokenization helps optimize prompt length and structure. For example, &#8220;prompt engineering&#8221; might be tokenized as [&#8220;prompt&#8221;, &#8221; engineering&#8221;] or [&#8220;pr&#8221;, &#8220;ompt&#8221;, &#8221; eng&#8221;, &#8220;ineering&#8221;] depending on the model.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Context Windows:<\/strong> Each model has a maximum context length (e.g., 8K, 32K, or 128K tokens). Effective prompt engineers design strategies to work within these constraints while maintaining coherent communication.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Attention Mechanisms:<\/strong> Models focus on different parts of the input when generating responses. Strategic placement of critical information can significantly impact output quality.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Natural Language Processing Fundamentals<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Modern prompt engineering builds on decades of NLP research. Essential concepts include:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Semantic Understanding:<\/strong> Models don&#8217;t just match keywords but understand meaning, context, and intent. This enables sophisticated reasoning but also requires careful prompt construction to avoid misinterpretation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Few-Shot Learning:<\/strong> LLMs can learn new tasks from examples within prompts. Understanding how to structure examples for optimal learning is crucial for complex applications.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Chain-of-Thought Reasoning:<\/strong> Breaking complex problems into step-by-step reasoning chains dramatically improves model performance on analytical tasks.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Model Architectures and Capabilities<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Different models excel in different areas:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>GPT-Family Models:<\/strong> Strong general-purpose reasoning, creative writing, and code generation <strong>Claude:<\/strong> Enhanced safety features and nuanced reasoning capabilities<br><strong>Gemini:<\/strong> Multimodal capabilities and real-time information access <strong>Specialized Models:<\/strong> Domain-specific models for medical, legal, or scientific applications<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Understanding these strengths helps prompt engineers select appropriate models and tailor prompts accordingly.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Learning Pathways: From Beginner to Expert<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Path 1: The Self-Directed Learner<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Many successful prompt engineers start with hands-on experimentation. This approach emphasizes practical learning through direct interaction with AI models.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Phase 1: Foundation Building (Weeks 1-4)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Create accounts on major AI platforms (ChatGPT, Claude, Gemini)<\/li>\n\n\n\n<li>Practice basic prompt structures and observe response patterns<\/li>\n\n\n\n<li>Study successful prompts from online communities and repositories<\/li>\n\n\n\n<li>Learn fundamental AI concepts through accessible resources<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Phase 2: Systematic Experimentation (Weeks 5-12)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Implement structured testing methodologies<\/li>\n\n\n\n<li>Document prompt variations and their outcomes<\/li>\n\n\n\n<li>Focus on specific use cases relevant to your interests or profession<\/li>\n\n\n\n<li>Begin learning evaluation techniques and metrics<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Phase 3: Advanced Techniques (Weeks 13-24)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Master complex prompting strategies (chain-of-thought, constitutional AI)<\/li>\n\n\n\n<li>Develop domain-specific expertise<\/li>\n\n\n\n<li>Create reusable prompt templates and frameworks<\/li>\n\n\n\n<li>Contribute to open-source projects or online communities<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Path 2: The Academic Approach<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">For those preferring structured learning, several academic and professional programs now offer prompt engineering curricula.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Formal Education Options:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>MIT&#8217;s &#8220;Introduction to Machine Learning&#8221; with AI communication modules<\/li>\n\n\n\n<li>Stanford&#8217;s &#8220;CS224N: Natural Language Processing with Deep Learning&#8221;<\/li>\n\n\n\n<li>Coursera&#8217;s &#8220;Prompt Engineering for ChatGPT&#8221; specialization<\/li>\n\n\n\n<li>edX&#8217;s &#8220;Artificial Intelligence Ethics and Governance&#8221; programs<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Research-Based Learning:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Study recent papers on prompt optimization and AI alignment<\/li>\n\n\n\n<li>Participate in academic conferences (NeurIPS, ICML, ACL)<\/li>\n\n\n\n<li>Engage with research communities on platforms like arXiv and Papers with Code<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Path 3: The Professional Transition<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Many prompt engineers transition from adjacent fields, leveraging existing expertise while developing new AI communication skills.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Common Background Transitions:<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Software Developers:<\/strong> Apply engineering principles to prompt design, focusing on modularity, testing, and optimization<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Data Scientists:<\/strong> Leverage statistical thinking and experimental design for prompt evaluation and improvement<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>UX\/UI Designers:<\/strong> Apply user experience principles to human-AI interaction design<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Content Creators:<\/strong> Utilize writing and communication skills for creative and marketing applications<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Domain Experts:<\/strong> Transform deep subject matter knowledge into specialized AI applications<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Essential Prompt Engineering Techniques<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Role-Based Prompting<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">One of the most effective techniques involves assigning specific roles to AI models. This approach leverages the model&#8217;s training on diverse professional contexts.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example Structure:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>You are a &#91;specific role] with &#91;relevant expertise]. \nYour task is to &#91;specific objective].\nConsider these constraints: &#91;limitations\/requirements]\nProvide your response in the format: &#91;desired output structure]\n<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Advanced Role Prompting:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>You are a senior data scientist with 10 years of experience in machine learning model deployment. You specialize in production MLOps pipelines and have deep knowledge of AWS SageMaker, Docker, and Kubernetes. \n\nYour task is to review the following model architecture and provide recommendations for production deployment, considering scalability, monitoring, and cost optimization.\n\nConsider these constraints:\n- Budget limit of $5,000\/month for infrastructure\n- Expected traffic of 100,000 API calls\/day\n- Requirement for 99.9% uptime\n- Need for real-time inference (&lt; 200ms response time)\n\nProvide your response in this format:\n1. Architecture Assessment\n2. Deployment Recommendations\n3. Monitoring Strategy\n4. Cost Analysis\n5. Risk Mitigation Plan\n<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Chain-of-Thought (CoT) Prompting<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This technique guides models through step-by-step reasoning processes, dramatically improving performance on complex analytical tasks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Basic CoT Structure:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>Problem: &#91;Complex question or task]\n\nLet's think through this step by step:\n1. First, I need to understand &#91;aspect 1]\n2. Then, I should consider &#91;aspect 2]\n3. Next, I'll analyze &#91;aspect 3]\n4. Finally, I'll synthesize &#91;conclusion]\n\nPlease work through each step explicitly before providing your final answer.\n<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Constitutional AI and Self-Correction<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Advanced prompt engineering incorporates self-reflection and correction mechanisms, helping models produce more accurate and aligned outputs.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Constitutional Prompting Example:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>Initial Task: &#91;Primary objective]\n\nBefore providing your final answer, please:\n1. Review your response for potential biases or assumptions\n2. Check your reasoning for logical consistency\n3. Consider alternative perspectives or approaches\n4. Verify that your answer directly addresses the question\n5. Ensure your response is helpful and harmless\n\nIf you identify any issues in steps 1-5, revise your response accordingly.\n<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Multi-Shot Learning and Example Crafting<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Providing high-quality examples dramatically improves model performance, especially for specialized tasks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example Structure Best Practices:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use diverse, representative examples<\/li>\n\n\n\n<li>Include both positive and negative cases<\/li>\n\n\n\n<li>Provide clear input-output mappings<\/li>\n\n\n\n<li>Explain the reasoning behind each example when helpful<\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code>Here are examples of excellent technical documentation:\n\nExample 1:\nInput: Function that calculates compound interest\nOutput: \n\"\"\"\ncalculate_compound_interest(principal, rate, time, compound_frequency)\n\nCalculates compound interest using the standard formula.\n\nArgs:\n    principal (float): Initial investment amount in dollars\n    rate (float): Annual interest rate as decimal (e.g., 0.05 for 5%)\n    time (int): Investment period in years\n    compound_frequency (int): Number of times interest compounds per year\n\nReturns:\n    float: Final amount after compound interest\n\nRaises:\n    ValueError: If any parameter is negative\n    \nExample:\n    &gt;&gt;&gt; calculate_compound_interest(1000, 0.05, 10, 12)\n    1643.62\n\"\"\"\n\nNow document this function: &#91;new function to document]\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">Tools and Platforms for Prompt Engineers<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Development Environments<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Prompt IDEs and Playgrounds:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>OpenAI Playground:<\/strong> Interactive environment for GPT model experimentation<\/li>\n\n\n\n<li><strong>Anthropic Console:<\/strong> Claude-specific development interface with safety features<\/li>\n\n\n\n<li><strong>LangChain:<\/strong> Framework for building LLM-powered applications<\/li>\n\n\n\n<li><strong>PromptLayer:<\/strong> Version control and analytics for prompt development<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Prompt Management Platforms:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Humanloop:<\/strong> End-to-end prompt engineering workflow management<\/li>\n\n\n\n<li><strong>Scale AI:<\/strong> Enterprise-grade prompt optimization and deployment<\/li>\n\n\n\n<li><strong>Weights &amp; Biases:<\/strong> Experiment tracking and prompt performance monitoring<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Evaluation and Testing Tools<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Rigorous testing is essential for production prompt engineering. Key tools include:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Automated Evaluation Frameworks:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>HELM (Holistic Evaluation of Language Models):<\/strong> Comprehensive benchmark suite<\/li>\n\n\n\n<li><strong>EleutherAI&#8217;s Language Model Evaluation Harness:<\/strong> Open-source evaluation toolkit<\/li>\n\n\n\n<li><strong>Custom evaluation scripts:<\/strong> Domain-specific testing frameworks<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Human Evaluation Platforms:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Surge AI:<\/strong> Professional human evaluation services<\/li>\n\n\n\n<li><strong>Scale AI&#8217;s Rapid:<\/strong> Crowdsourced prompt testing and optimization<\/li>\n\n\n\n<li><strong>Amazon Mechanical Turk:<\/strong> Custom evaluation task deployment<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Prompt Libraries and Resources<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Community Resources:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Awesome Prompts (GitHub):<\/strong> Curated collection of effective prompts<\/li>\n\n\n\n<li><strong>PromptBase:<\/strong> Marketplace for prompt templates and strategies<\/li>\n\n\n\n<li><strong>r\/PromptEngineering:<\/strong> Active community for sharing techniques and insights<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Professional Resources:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>OpenAI Cookbook:<\/strong> Official examples and best practices<\/li>\n\n\n\n<li><strong>Anthropic&#8217;s Constitutional AI papers:<\/strong> Research on AI alignment and safety<\/li>\n\n\n\n<li><strong>Google AI&#8217;s Model Garden:<\/strong> Pre-trained models and prompt examples<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Building Your Prompt Engineering Career<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Entry-Level Positions and Skills<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Junior Prompt Engineer Roles:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Salary Range: $75,000 &#8211; $120,000<\/li>\n\n\n\n<li>Key Responsibilities: Basic prompt crafting, testing, and optimization<\/li>\n\n\n\n<li>Required Skills: Understanding of LLMs, basic programming, strong written communication<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Skill Development Priorities:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Master fundamental prompting techniques<\/li>\n\n\n\n<li>Learn evaluation methodologies and metrics<\/li>\n\n\n\n<li>Develop proficiency with major AI platforms<\/li>\n\n\n\n<li>Build domain expertise in target industries<\/li>\n\n\n\n<li>Create a portfolio of successful prompt engineering projects<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Mid-Level Career Development<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Senior Prompt Engineer Positions:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Salary Range: $120,000 &#8211; $200,000<\/li>\n\n\n\n<li>Responsibilities: Complex system design, team leadership, strategic AI implementation<\/li>\n\n\n\n<li>Advanced Skills: Multi-modal prompting, fine-tuning integration, production deployment<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Career Enhancement Strategies:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Specialize in high-value domains (healthcare, finance, legal)<\/li>\n\n\n\n<li>Develop cross-functional expertise (MLOps, product management, UX design)<\/li>\n\n\n\n<li>Contribute to open-source projects and research publications<\/li>\n\n\n\n<li>Build thought leadership through content creation and speaking engagements<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Expert-Level Opportunities<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Principal\/Staff Prompt Engineer Roles:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Salary Range: $200,000 &#8211; $335,000+<\/li>\n\n\n\n<li>Focus Areas: Research and development, architectural decision-making, industry innovation<\/li>\n\n\n\n<li>Elite Skills: Novel technique development, academic research collaboration, strategic consulting<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Leadership and Entrepreneurship:<\/strong> Many expert prompt engineers transition into:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Consulting roles:<\/strong> Helping organizations implement AI strategies<\/li>\n\n\n\n<li><strong>Product leadership:<\/strong> Driving AI-powered product development<\/li>\n\n\n\n<li><strong>Research positions:<\/strong> Contributing to academic and industry research<\/li>\n\n\n\n<li><strong>Entrepreneurship:<\/strong> Founding AI-focused startups and services<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Building Your Professional Network<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Industry Communities:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>AI\/ML conferences:<\/strong> NeurIPS, ICML, ICLR for research focus<\/li>\n\n\n\n<li><strong>Industry events:<\/strong> AI Summit, Transform, MLOps World for practical applications<\/li>\n\n\n\n<li><strong>Professional associations:<\/strong> ACM, IEEE Computer Society for formal networking<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Online Engagement:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>LinkedIn:<\/strong> Share insights and connect with industry professionals<\/li>\n\n\n\n<li><strong>Twitter\/X:<\/strong> Follow AI researchers and participate in technical discussions<\/li>\n\n\n\n<li><strong>GitHub:<\/strong> Contribute to open-source projects and showcase technical skills<\/li>\n\n\n\n<li><strong>Medium\/Substack:<\/strong> Publish technical content and case studies<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Advanced Techniques and Emerging Trends<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Multi-Modal Prompt Engineering<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">As AI models increasingly handle images, audio, and video alongside text, prompt engineers must adapt their techniques:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Vision-Language Models:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Crafting prompts that effectively combine visual and textual information<\/li>\n\n\n\n<li>Understanding how different modalities interact within unified models<\/li>\n\n\n\n<li>Optimizing for tasks like image analysis, document understanding, and visual reasoning<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Audio and Speech Integration:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Developing prompts for speech-to-text and text-to-speech applications<\/li>\n\n\n\n<li>Managing conversation flow in voice-based AI assistants<\/li>\n\n\n\n<li>Handling temporal information in audio processing tasks<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Retrieval-Augmented Generation (RAG)<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Modern applications often combine LLMs with external knowledge sources:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>RAG Prompt Engineering:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Designing queries that effectively retrieve relevant information<\/li>\n\n\n\n<li>Crafting prompts that synthesize retrieved content with generated responses<\/li>\n\n\n\n<li>Managing context window limitations with large knowledge bases<\/li>\n\n\n\n<li>Handling potential conflicts between retrieved and parametric knowledge<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Vector Database Integration:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Understanding semantic search and embedding spaces<\/li>\n\n\n\n<li>Optimizing retrieval queries for different vector database systems<\/li>\n\n\n\n<li>Balancing retrieval precision and recall for specific applications<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Constitutional AI and Safety<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">As AI systems become more powerful, safety considerations become paramount:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Safety-First Prompting:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Incorporating constitutional principles into prompt design<\/li>\n\n\n\n<li>Building robust guardrails against harmful outputs<\/li>\n\n\n\n<li>Testing for edge cases and adversarial inputs<\/li>\n\n\n\n<li>Ensuring alignment with organizational values and ethical guidelines<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Bias Mitigation:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Identifying and addressing potential biases in prompt design<\/li>\n\n\n\n<li>Testing prompts across diverse demographic groups and use cases<\/li>\n\n\n\n<li>Implementing feedback loops for continuous bias monitoring<\/li>\n\n\n\n<li>Collaborating with ethics teams and diverse stakeholders<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Automated Prompt Optimization<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The field is moving toward automated prompt discovery and optimization:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Prompt Evolution Techniques:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Genetic algorithms for prompt optimization<\/li>\n\n\n\n<li>Reinforcement learning from human feedback (RLHF) integration<\/li>\n\n\n\n<li>Automated prompt generation and testing frameworks<\/li>\n\n\n\n<li>Meta-learning approaches for prompt adaptation<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>AI-Assisted Prompt Engineering:<\/strong> Many prompt engineers now use AI to improve their own prompts:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>You are an expert prompt engineer. Analyze the following prompt and suggest improvements:\n\nOriginal Prompt: &#91;insert prompt]\n\nPlease provide:\n1. Clarity Assessment: Rate clarity (1-10) and explain issues\n2. Specificity Analysis: Identify areas needing more precision\n3. Structure Evaluation: Assess organization and flow\n4. Optimization Suggestions: Provide 3-5 specific improvements\n5. Rewritten Version: Create an improved version incorporating your suggestions\n\nFocus on making the prompt more effective at achieving its intended goal while maintaining readability and user-friendliness.\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">Measuring Success: Evaluation and Metrics<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Quantitative Evaluation Methods<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Task-Specific Metrics:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Accuracy:<\/strong> For classification and factual tasks<\/li>\n\n\n\n<li><strong>BLEU\/ROUGE scores:<\/strong> For text generation quality<\/li>\n\n\n\n<li><strong>Semantic similarity:<\/strong> Using embedding-based comparisons<\/li>\n\n\n\n<li><strong>Task completion rate:<\/strong> For complex, multi-step processes<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Efficiency Metrics:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Token usage optimization:<\/strong> Minimizing cost while maintaining quality<\/li>\n\n\n\n<li><strong>Response time:<\/strong> Balancing prompt complexity with speed requirements<\/li>\n\n\n\n<li><strong>Iteration count:<\/strong> Measuring how many attempts are needed for satisfactory results<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Qualitative Assessment Frameworks<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Human Evaluation Criteria:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Relevance:<\/strong> How well does the output address the prompt&#8217;s intent?<\/li>\n\n\n\n<li><strong>Coherence:<\/strong> Is the response logically structured and easy to follow?<\/li>\n\n\n\n<li><strong>Creativity:<\/strong> For creative tasks, does the output demonstrate originality?<\/li>\n\n\n\n<li><strong>Safety:<\/strong> Does the response avoid harmful, biased, or inappropriate content?<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Expert Review Processes:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Domain expert validation for specialized applications<\/li>\n\n\n\n<li>Blind comparison studies between different prompt approaches<\/li>\n\n\n\n<li>Long-term user satisfaction tracking and feedback collection<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">A\/B Testing for Prompts<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Systematic testing approaches help identify optimal prompt strategies:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Experimental Design:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>Hypothesis: Adding explicit reasoning instructions improves analytical accuracy\n\nControl Group: Standard analytical prompt\nTest Group: Same prompt + \"Please show your reasoning step-by-step\"\n\nSuccess Metrics:\n- Accuracy of final conclusions (primary)\n- Quality of reasoning process (secondary)\n- User satisfaction ratings (tertiary)\n\nSample Size: 200 evaluations per group\nStatistical Significance: p &lt; 0.05\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">Future of Prompt Engineering<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Emerging Technologies and Techniques<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Multimodal AI Integration:<\/strong> The next generation of AI systems will seamlessly integrate text, images, audio, and video. Prompt engineers will need to develop new techniques for orchestrating these complex interactions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Autonomous Agent Development:<\/strong> As AI agents become more autonomous, prompt engineering will evolve toward higher-level goal specification and constraint definition rather than step-by-step instruction.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Real-Time Adaptation:<\/strong> Future systems may dynamically adapt their behavior based on user feedback and context changes, requiring prompt engineers to design flexible, self-modifying communication strategies.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Industry Evolution and Opportunities<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Specialized Domains:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Healthcare:<\/strong> Medical diagnosis assistance and treatment planning<\/li>\n\n\n\n<li><strong>Legal:<\/strong> Contract analysis and legal research automation<\/li>\n\n\n\n<li><strong>Finance:<\/strong> Risk assessment and regulatory compliance<\/li>\n\n\n\n<li><strong>Education:<\/strong> Personalized tutoring and curriculum development<\/li>\n\n\n\n<li><strong>Creative Industries:<\/strong> Content generation and artistic collaboration<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Regulatory and Ethical Considerations:<\/strong> As governments develop AI regulations, prompt engineers will play crucial roles in ensuring compliance and ethical AI deployment. This includes understanding data privacy requirements, bias mitigation standards, and transparency obligations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Skills for the Future<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Technical Evolution:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Programming Integration:<\/strong> Understanding how to integrate prompts with traditional software development<\/li>\n\n\n\n<li><strong>Model Fine-tuning:<\/strong> Combining prompt engineering with custom model training<\/li>\n\n\n\n<li><strong>Cross-Platform Optimization:<\/strong> Designing prompts that work effectively across different AI systems<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Strategic Thinking:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Business Alignment:<\/strong> Connecting AI capabilities with organizational objectives<\/li>\n\n\n\n<li><strong>User Experience Design:<\/strong> Creating intuitive human-AI interfaces<\/li>\n\n\n\n<li><strong>Change Management:<\/strong> Helping organizations adapt to AI-augmented workflows<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Practical Next Steps: Your Journey Begins Now<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">30-Day Quick Start Plan<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Week 1: Foundation<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Create accounts on 3 major AI platforms<\/li>\n\n\n\n<li>Complete 10 basic prompting exercises daily<\/li>\n\n\n\n<li>Join 2 online prompt engineering communities<\/li>\n\n\n\n<li>Read 5 foundational articles on AI and prompt engineering<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Week 2: Structured Learning<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Choose one specialized domain for focus<\/li>\n\n\n\n<li>Practice role-based prompting techniques<\/li>\n\n\n\n<li>Experiment with chain-of-thought reasoning<\/li>\n\n\n\n<li>Document your most successful prompts<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Week 3: Advanced Techniques<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Try multi-shot learning with complex examples<\/li>\n\n\n\n<li>Practice constitutional AI and self-correction methods<\/li>\n\n\n\n<li>Test prompt performance with evaluation metrics<\/li>\n\n\n\n<li>Start building a personal prompt library<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Week 4: Portfolio Development<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Create 3 polished prompt engineering projects<\/li>\n\n\n\n<li>Write a blog post about your learning experience<\/li>\n\n\n\n<li>Connect with 10 professionals in the field<\/li>\n\n\n\n<li>Plan your continued learning path<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Building Your First Portfolio<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Project Categories:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Content Creation:<\/strong> Develop prompts for marketing copy, technical documentation, or creative writing<\/li>\n\n\n\n<li><strong>Data Analysis:<\/strong> Create prompts for interpreting datasets, generating insights, or building reports<\/li>\n\n\n\n<li><strong>Code Generation:<\/strong> Design prompts that generate, review, or debug code in various programming languages<\/li>\n\n\n\n<li><strong>Domain Expertise:<\/strong> Apply prompt engineering to your existing professional knowledge area<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Documentation Best Practices:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Clearly state the problem each prompt solves<\/li>\n\n\n\n<li>Show before\/after examples demonstrating improvement<\/li>\n\n\n\n<li>Include metrics and evaluation results where possible<\/li>\n\n\n\n<li>Explain your design decisions and iteration process<\/li>\n\n\n\n<li>Share lessons learned and future optimization ideas<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Continuous Learning Resources<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Essential Reading:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>&#8220;The Prompt Engineering Guide&#8221; by DAIR.AI<\/li>\n\n\n\n<li>OpenAI&#8217;s GPT best practices documentation<\/li>\n\n\n\n<li>Anthropic&#8217;s research publications on constitutional AI<\/li>\n\n\n\n<li>Regular arXiv papers on prompt optimization and AI alignment<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Hands-On Practice:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Kaggle competitions involving LLM applications<\/li>\n\n\n\n<li>Open-source contributions to prompt engineering tools<\/li>\n\n\n\n<li>Personal projects that solve real-world problems<\/li>\n\n\n\n<li>Collaboration with others on complex prompt engineering challenges<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion: Your Prompt Engineering Future Awaits<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The journey from zero to prompt engineer is both challenging and rewarding, offering opportunities to shape the future of human-AI collaboration. As we&#8217;ve explored throughout this comprehensive guide, success in prompt engineering requires a unique combination of technical understanding, creative communication skills, and strategic thinking.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The field is evolving rapidly, with new techniques, tools, and applications emerging regularly. This constant evolution means that successful prompt engineers must be committed to continuous learning and adaptation. However, it also means that early adopters and dedicated practitioners have tremendous opportunities to influence the direction of this emerging discipline.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Whether you&#8217;re starting from a technical background, transitioning from another field, or beginning your career entirely, the fundamental principles remain the same: understand your tools, practice systematically, measure your results, and never stop learning. The investment you make today in developing prompt engineering skills will pay dividends as AI becomes increasingly central to business operations, creative processes, and problem-solving across industries.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The future belongs to those who can effectively communicate with artificial intelligence, transforming human intent into AI action. Your journey to becoming a skilled prompt engineer starts with the next prompt you write. Make it count.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Ready to accelerate your prompt engineering journey?<\/strong> Join the Prompt Bestie community for weekly insights, expert interviews, and hands-on tutorials. Subscribe to our newsletter and follow us on social media for the latest developments in AI communication and prompt optimization.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Related Articles:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>&#8220;The Complete Guide to Chain-of-Thought Prompting&#8221;<\/li>\n\n\n\n<li>&#8220;Constitutional AI: Building Safer AI Systems Through Better Prompts&#8221;<\/li>\n\n\n\n<li>&#8220;Multi-Modal Prompt Engineering: Beyond Text-Only AI&#8221;<\/li>\n\n\n\n<li>&#8220;Career Spotlight: Day in the Life of a Senior Prompt Engineer&#8221;<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Sources and Further Reading:<\/em><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Wei, J., et al. (2022). &#8220;Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.&#8221; arXiv preprint arXiv:2201.11903.<\/li>\n\n\n\n<li>Bai, Y., et al. (2022). &#8220;Constitutional AI: Harmlessness from AI Feedback.&#8221; arXiv preprint arXiv:2212.08073.<\/li>\n\n\n\n<li>OpenAI. (2023). &#8220;GPT-4 Technical Report.&#8221; OpenAI Research.<\/li>\n\n\n\n<li>Zhou, D., et al. (2022). &#8220;Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.&#8221; arXiv preprint arXiv:2205.10625.<\/li>\n\n\n\n<li>Liu, P., et al. (2023). &#8220;Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing.&#8221; ACM Computing Surveys.<\/li>\n\n\n\n<li>Anthropic. (2023). &#8220;Claude&#8217;s Constitution.&#8221; Anthropic Safety Research.<\/li>\n\n\n\n<li>Google AI. (2023). &#8220;PaLM 2 Technical Report.&#8221; Google Research.<\/li>\n\n\n\n<li>Schulman, J., et al. (2022). &#8220;ChatGPT: Optimizing Language Models for Dialogue.&#8221; OpenAI Blog.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Master prompt engineering from beginner to expert. Learn essential skills, tools, and career paths in this comprehensive 2024 guide to AI communication.<\/p>","protected":false},"author":1,"featured_media":3591,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_crdt_document":"","footnotes":""},"categories":[14],"tags":[],"class_list":["post-3589","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-prompt-engineering"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/promptbestie.com\/es\/wp-json\/wp\/v2\/posts\/3589","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/promptbestie.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/promptbestie.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/promptbestie.com\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/promptbestie.com\/es\/wp-json\/wp\/v2\/comments?post=3589"}],"version-history":[{"count":3,"href":"https:\/\/promptbestie.com\/es\/wp-json\/wp\/v2\/posts\/3589\/revisions"}],"predecessor-version":[{"id":3596,"href":"https:\/\/promptbestie.com\/es\/wp-json\/wp\/v2\/posts\/3589\/revisions\/3596"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/promptbestie.com\/es\/wp-json\/wp\/v2\/media\/3591"}],"wp:attachment":[{"href":"https:\/\/promptbestie.com\/es\/wp-json\/wp\/v2\/media?parent=3589"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/promptbestie.com\/es\/wp-json\/wp\/v2\/categories?post=3589"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/promptbestie.com\/es\/wp-json\/wp\/v2\/tags?post=3589"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}