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🧠 Prompt Engineering Master Course — 2026 LLM Ecosystem

A comprehensive, open academic course on LLM prompt engineering covering the full 2026 model ecosystem — frontier proprietary (Claude, GPT, Gemini, Grok), Chinese frontier (DeepSeek, Kimi, MiniMax, Qwen, GLM), open-weights (Llama, Qwen, Gemma, Mistral, DeepSeek, Phi), and local deployment (Ollama, llama.cpp, MLX, vLLM).

Vendor-agnostic by default. Cross-API translation tables for every technique. Bilingual ES + EN. Versioned content with changelog. Comparable in scope to Anthropic Academy, Google ML Crash Course, DeepLearning.AI Generative AI courses — without vendor lock-in.

🔗 Live demo: gs-run.github.io/prompt-engineering-course

📋 Current version: 2.2.1 (see CHANGELOG)


📚 Course structure

The course is organised into 14 academic blocks + an orientation block + workshop + capstone. Each block is a standalone HTML page (~7-67 KB) loaded on demand, opening with front-matter (icon, level, time estimate, learning objectives) and closing with bibliographic references + a 5-question knowledge check.

# Block Level Time
0 Orientation (setup, prerequisites) beginner 20 min
I Foundations (LLM mechanics, ecosystem) beginner 90 min
II Prompt Engineering Core beginner 180 min
III Advanced Reasoning + Tools intermediate 150 min
IV Production Engineering intermediate 200 min
V Agents + Multi-agent intermediate 180 min
VI Infrastructure + RAG deep-dive intermediate 150 min
VII Local Models + Quantization + Privacy advanced 200 min
VIII Cross-Model Patterns advanced 60 min
IX Benchmarks + Evaluation intermediate 90 min
X Safety, Ethics, Regulation intermediate 90 min
XI Applied Industry + Roles intermediate 200 min
XII Future + Research Frontier reading 60 min
XIII Practical Workshop (Bando B) all 180 min
XIV Capstone Projects (4 end-to-end) capstone ~10h × 4

Total study time: ~30 hours across 4-6 weeks at 2-3 hour sessions.

🛠️ Interactive tools

  • Token Counter — real-time token estimation.
  • Cost Calculator — 18 frontier + OSS models grouped by region (US / China / EU), with caching scenarios.
  • Prompt Diff Comparator — A/B two prompts with 5-criteria scoring.
  • Prompt Simulator — toggle 6 techniques and see simulated response quality.
  • 5 Coding Exercises with real-time scoring.
  • 92 quizzes total — 22 inline formative + 70 end-of-block knowledge checks.
  • Glossary — 64 technical terms ES + EN with live filter, linked from every block.

🚀 Quick start

git clone https://github.com/GS-RUN/prompt-engineering-course.git
cd prompt-engineering-course
npx serve .
# open http://localhost:3000

Or just open index.html directly in a browser — no build step, no dependencies.


🏗️ Project structure

prompt-engineering-course/
├── index.html                  # Landing (35 KB) — hero + 17 block cards + course philosophy
├── modules/
│   ├── 00-orientation.html     # Setup, prerequisites, environment
│   ├── 01-foundations.html     # LLM mechanics + 2026 ecosystem
│   ├── 02-prompt-core.html     # Anatomy, roles, clarity, examples, XML, CoT, output
│   ├── 03-advanced-reasoning.html   # Thinking, tool use, multimodal
│   ├── 04-production.html      # Structured outputs, caching, evals, security, costs
│   ├── 05-agents.html          # Skills, sub-agents, MCP, multi-agent patterns
│   ├── 06-infrastructure.html  # Chunking, vector DBs, deployment, monitoring
│   ├── 07-local-quant.html     # GPTQ/AWQ/GGUF, fine-tuning, privacy
│   ├── 08-cross-model.html     # Vendor-agnostic patterns, fallback, routing
│   ├── 09-benchmarks.html      # MMLU/HumanEval/SWE-bench/ARC-AGI + eval design
│   ├── 10-safety.html          # Hallucination, red teaming, EU AI Act
│   ├── 11-industry.html        # Code review, support, extraction, by sector + role
│   ├── 12-future.html          # Research frontiers, expected models, AI + science
│   ├── 13-workshop.html        # Bando B: project, linter, library, evolution
│   ├── 14-capstone.html        # 4 end-to-end project specs with rubrics
│   ├── glossary.html           # 64 terms ES + EN, live filter
│   └── tools.html              # Token counter, cost calc, prompt diff
├── css/
│   ├── style.css               # Carbón + Ámbar palette, dark + light themes
│   └── bando-b.css             # Workshop-specific styling
├── js/
│   ├── shared/
│   │   ├── manifest.js         # Single source of truth (block list, objectives, sections)
│   │   ├── sidebar.js          # Generates sidebar dynamically per page
│   │   ├── landing.js          # Renders block grid on home page
│   │   ├── references.js       # Per-block bibliography
│   │   └── glossary-data.js    # 64 term definitions
│   ├── app.js                  # Theme controller, defensive widget instantiation
│   ├── i18n.js                 # ES + EN toggle (CSS-driven via lang-blocks)
│   ├── quiz.js                 # 92 multiple-choice quizzes
│   ├── exercises.js            # 5 interactive scoring exercises
│   ├── simulator.js            # Technique-toggle prompt simulator
│   ├── diagrams.js             # Animated Canvas diagrams
│   ├── token-tools.js          # Token counter + cost calculator
│   ├── prompt-diff.js          # A/B prompt comparator
│   ├── linter.js               # Prompt linter (Bando B)
│   ├── library.js              # Prompt library (Bando B)
│   ├── evolution.js            # Prompt evolution tracker (Bando B)
│   ├── project.js              # User project panel (Bando B)
│   └── glossary.js             # In-text glossary popovers
├── scripts/
│   ├── split_modules.js        # One-shot: extract sections from index.legacy.html into modules/
│   ├── add_knowledge_checks.js # One-shot: insert kc-NN anchors into all module pages
│   └── wrap_lang_blocks.js     # One-shot: wrap section bodies in lang-block divs
├── CHANGELOG.md
├── README.md
└── index.legacy.html           # v1 monolithic backup (will be removed in 2.3)

Architectural highlights

  • Multi-page split: initial landing 35 KB; each block 7-67 KB. Browser caches CSS/JS across pages.
  • Single source of truth: js/shared/manifest.js declares the block structure. The sidebar, landing, and split scripts all consume it.
  • Bilingual via CSS: every section body is wrapped in <div class="lang-block" data-lang="es|en">. The <html> element's data-i18n-lang attribute drives visibility. No JS replacement of textContent.
  • Defensive JS instantiation: widget engines are only constructed if their class is loaded on the current page (each module imports only the JS it needs).
  • Versioned content: every claim is dated 2026-01 to 2026-05. The "model + hardware version policy" in Block XIV instructs readers to substitute upward (e.g. Opus 4.7 → Opus 5+ when shipped).

🌐 Languages

Click ES / EN in the sidebar to switch. Coverage:

  • Every section body has parallel ES + EN content. Sections without an EN translation fall back to ES with a banner.
  • Sidebar, landing cards, glossary, knowledge checks, references — all bilingual.
  • Language preference saved to localStorage and applied via <html data-i18n-lang="es|en">.
  • Convention: prompt examples appear in the active language. The course flags honestly that production prompts often run 5-15% better in English on small open-weights models.

🎨 Design

  • Carbón + Ámbar palette (warm dark default, light theme available).
  • CSS-only animated background (gradient orbs, subtle grid).
  • Glassmorphism cards with backdrop blur.
  • Responsive: sidebar collapses to a horizontal bar on mobile.
  • Difficulty badges (beginner / intermediate / advanced / capstone / reference / reading).
  • Animated progress bar at the top.

📖 Sources

The course is researched and compiled from official documentation. Each block also has its own per-block bibliography in js/shared/references.js with direct links to papers, RFCs and official docs.

Primary sources (provider documentation):

Foundational papers (linked in per-block references):

  • Vaswani et al. — Attention Is All You Need (2017)
  • Brown et al. — Language Models are Few-Shot Learners (GPT-3, 2020)
  • Wei et al. — Chain-of-Thought Prompting (2022)
  • Yao et al. — ReAct: Synergizing Reasoning and Acting (2022)
  • Bai et al. — Constitutional AI (Anthropic, 2022)
  • Frantar et al. — GPTQ (2022); Lin et al. — AWQ (2023); Dettmers et al. — QLoRA (2023)
  • Hendrycks et al. — MMLU (2020); Chen et al. — HumanEval (2021); Rein et al. — GPQA (2023)
  • Greshake et al. — Indirect Prompt Injection (2023)

🤝 Contributing

Pull requests welcome. The course is versioned (semver-ish): patch for fixes/typos, minor for new content, major for architectural changes.

Architecture contracts:

  • Block structure → edit js/shared/manifest.js. Re-run node scripts/split_modules.js if changing legacy section ranges.
  • Knowledge checks → add to js/quiz.js with section: "kc-NN".
  • Glossary terms → add to js/shared/glossary-data.js.
  • Per-block references → edit js/shared/references.js.
  • New module page → use the same shell as existing pages (<head> includes, <body data-page-block="NN">, <nav id="sidebar"> populated by sidebar.js, <main id="content">, references section, knowledge-check anchor).

When to bump version:

  • 2.2.1 → 2.2.2: typos, refactors, link fixes.
  • 2.2.x → 2.3.0: new section, new module, content rewrite.
  • 2.x.y → 3.0.0: architectural change (manifest schema, breaking URL changes).

Always update CHANGELOG.md with the change. Version lives in js/shared/manifest.js and is shown in the sidebar foot + landing footer.


📄 License

MIT — use it, modify it, share it.


© 2026 Alonso J. Núñez · GS·RUN · Source code

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