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<title>Prompt Engineering Master Course — 2026 LLM Ecosystem</title>
<meta name="description" content="Curso académico de prompt engineering cubriendo el ecosistema completo 2026: Claude, GPT, Gemini, DeepSeek, Kimi, MiniMax, Qwen, GLM, Grok, Mistral, Llama, Gemma. 14 bloques + capstone, bilingüe ES/EN.">
<meta name="author" content="Alonso J. Núñez">
<meta name="keywords" content="prompt engineering, LLM, Claude, GPT, Gemini, DeepSeek, Kimi, MiniMax, Qwen, GLM, Grok, Mistral, Llama, Gemma, RAG, agents, MCP, fine-tuning, quantization, vendor-agnostic, course, 2026">

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<meta property="og:description" content="Open academic course covering Claude, GPT, Gemini, DeepSeek, Kimi, MiniMax, Qwen, GLM, Grok, Mistral, Llama, Gemma. 14 blocks · 4 capstones · 92 quizzes · ES + EN. MIT licensed.">
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<section class="course-hero">
  <h1 class="hero-title">
    <span class="lang-block" data-lang="es">Prompt Engineering Master Course</span>
    <span class="lang-block" data-lang="en">Prompt Engineering Master Course</span>
  </h1>
  <p class="hero-tagline">
    <span class="lang-block" data-lang="es">Curso académico abierto sobre el ecosistema LLM 2026 — agnóstico, profesional, bilingüe.</span>
    <span class="lang-block" data-lang="en">Open academic course on the 2026 LLM ecosystem — vendor-agnostic, professional, bilingual.</span>
  </p>
  <div class="hero-meta">
    <span class="hero-pill">📚 14 <span data-i18n="hero-blocks">bloques</span></span>
    <span class="hero-pill">⏱️ ~30 <span data-i18n="hero-hours">horas</span></span>
    <span class="hero-pill">🎓 <span data-i18n="hero-level">Nivel</span>: Beginner → Advanced</span>
    <span class="hero-pill">🌐 ES + EN</span>
  </div>
</section>

<section class="card card-accent">
  <h2 class="lang-block" data-lang="es" style="display:inline-block;">📋 Cómo recorrer el curso</h2>
  <h2 class="lang-block" data-lang="en" style="display:inline-block;">📋 How to navigate the course</h2>
  <div class="lang-block" data-lang="es">
    <p>Cada bloque es una página independiente con sus propios objetivos de aprendizaje, tiempo estimado y referencias bibliográficas. Si vienes nuevo al tema, sigue el orden 0 → I → II → III. Si ya tienes base, salta al bloque que necesites — los prerrequisitos están al principio de cada uno.</p>
    <ul>
      <li><strong>Bloques 0-II</strong> — Fundamentos. Sin esto el resto no se sostiene.</li>
      <li><strong>Bloques III-VI</strong> — Construcción técnica. Aquí aprendes a producir.</li>
      <li><strong>Bloques VII-X</strong> — Operaciones, privacidad, seguridad y medición.</li>
      <li><strong>Bloques XI-XIV</strong> — Aplicación industrial, futuro, taller práctico, capstone.</li>
    </ul>
    <p>El curso es <strong>agnóstico de vendor</strong>: cada técnica se enseña con tablas cross-API que muestran cómo se invoca en Claude, GPT-5, Gemini, DeepSeek, Kimi, MiniMax, Qwen, GLM, Grok, Mistral, Llama y Gemma.</p>
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  <div class="lang-block" data-lang="en">
    <p>Each block is a standalone page with its own learning objectives, time estimate and bibliographic references. If you're new to the topic, follow the order 0 → I → II → III. If you already have a base, jump to whichever block you need — prerequisites are listed at the start of each one.</p>
    <ul>
      <li><strong>Blocks 0-II</strong> — Foundations. The rest doesn't stand without these.</li>
      <li><strong>Blocks III-VI</strong> — Technical build-up. Where you learn to ship.</li>
      <li><strong>Blocks VII-X</strong> — Operations, privacy, safety and measurement.</li>
      <li><strong>Blocks XI-XIV</strong> — Industry applications, future, practical workshop, capstone.</li>
    </ul>
    <p>The course is <strong>vendor-agnostic</strong>: every technique is taught with cross-API tables showing how it's invoked in Claude, GPT-5, Gemini, DeepSeek, Kimi, MiniMax, Qwen, GLM, Grok, Mistral, Llama and Gemma.</p>
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<section>
  <h2 style="margin-top:36px;">
    <span class="lang-block" data-lang="es">🗂️ Bloques del curso</span>
    <span class="lang-block" data-lang="en">🗂️ Course blocks</span>
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<section class="card">
  <h3>
    <span class="lang-block" data-lang="es">📌 Sobre este curso</span>
    <span class="lang-block" data-lang="en">📌 About this course</span>
  </h3>
  <div class="lang-block" data-lang="es">
    <p>Este curso es un esfuerzo independiente para condensar todo lo que un profesional de software/datos/producto necesita saber sobre LLMs en 2026, desde fundamentos hasta producción real. Compite en alcance con cursos oficiales de Anthropic, Google, OpenAI y DeepLearning.AI, pero <strong>sin lock-in a un proveedor</strong>.</p>
    <p><strong>Para quién es:</strong> ingenieros que integran IA en producto, analistas/data scientists que la usan a diario, PMs y ejecutivos que toman decisiones sobre adopción, estudiantes en máster/doctorado en ML, formadores que necesitan material actualizado.</p>
    <p><strong>Política de actualización:</strong> el contenido se versiona (ver footer) y los cambios significativos se anotan en <a href="CHANGELOG.md">CHANGELOG</a>. Si encuentras algo desactualizado, abre issue en GitHub.</p>
  </div>
  <div class="lang-block" data-lang="en">
    <p>This course is an independent effort to condense everything a software / data / product professional needs to know about LLMs in 2026, from fundamentals to real production. It rivals in scope the official courses from Anthropic, Google, OpenAI and DeepLearning.AI, but <strong>with no vendor lock-in</strong>.</p>
    <p><strong>Who it's for:</strong> engineers integrating AI into product, analysts / data scientists using it daily, PMs and executives making adoption decisions, masters / PhD students in ML, instructors needing up-to-date material.</p>
    <p><strong>Update policy:</strong> content is versioned (see footer) and significant changes are listed in the <a href="CHANGELOG.md">CHANGELOG</a>. If you find something out of date, open a GitHub issue.</p>
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    <strong>Prompt Engineering Master Course — 2026 LLM Ecosystem</strong> ·
    <span data-i18n="footer-version">Versión</span> <span id="footer-version-tag">2.0.0</span> · 2026<br>
    <span class="lang-block" data-lang="es">Investigado y compilado desde documentación oficial de Anthropic, OpenAI, Google, DeepSeek, Moonshot, Alibaba (Qwen), Zhipu (GLM), MiniMax, Mistral, Meta, Microsoft, Cohere, OpenCode y DAIR.AI.</span>
    <span class="lang-block" data-lang="en">Researched and compiled from official documentation of Anthropic, OpenAI, Google, DeepSeek, Moonshot, Alibaba (Qwen), Zhipu (GLM), MiniMax, Mistral, Meta, Microsoft, Cohere, OpenCode and DAIR.AI.</span><br>
    © 2026 <a href="https://github.com/GS-RUN" target="_blank" rel="noopener">Alonso J. Núñez · GS·RUN</a> · <a href="https://github.com/GS-RUN/prompt-engineering-course" target="_blank" rel="noopener">Source code</a>
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