🧠 LLM Dev Curriculum

The open-source, community-driven roadmap to building with Large Language Models —

from your first "Hello, GPT" to shipping production AI systems.

⭐ If this helped you, star it — it helps others find it too.

🚀 Start Learning · 📚 All Modules · 🏗️ Projects · 🤝 Contribute · 💬 Community

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🌟 Why This Curriculum?

There are hundreds of LLM tutorials out there. Most are either:

• 🔴 Too shallow — "just call the API and you're done"
• 🔴 Too academic — dense math, no runnable code
• 🔴 Too opinionated — locked into one framework
• 🔴 Outdated — written in 2023 and never touched since

This curriculum is different. It's:

✅	What you get
Beginner → Production	Start from zero ML knowledge. End with a deployed, monitored AI system.
Framework-agnostic	OpenAI, Anthropic, HuggingFace, open-weight models — you learn all of them.
Hands-on first	Every concept has code. Every phase has a capstone project you can put on your portfolio.
Community-maintained	Updated by practitioners, not just educators. PRs welcome.
Clearly leveled	Every module is tagged 🟢 Beginner · 🟡 Intermediate · 🔴 Advanced — no surprises.

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🗺️ The Big Picture

YOU ARE HERE
     │
     ▼
┌──────────────────────────────────────────────────────────────┐
│  PHASE 0 · Foundations                    🟢 No prerequisites │
│  What LLMs are, how transformers work, tokens & embeddings   │
└───────────────────────────┬──────────────────────────────────┘
                            │
                            ▼
┌──────────────────────────────────────────────────────────────┐
│  PHASE 1 · Prompting                           🟢 Beginner   │
│  Prompt engineering, CoT, few-shot, system prompts           │
└───────────────────────────┬──────────────────────────────────┘
                            │
                            ▼
┌──────────────────────────────────────────────────────────────┐
│  PHASE 2 · APIs & Integrations                 🟢 Beginner   │
│  Call LLMs from code. Build your first real app.             │
└───────────┬───────────────┴───────────────┬──────────────────┘
            │                               │
            ▼                               ▼
┌───────────────────────┐       ┌───────────────────────────┐
│  PHASE 3 · RAG  🟡    │       │  PHASE 4 · Fine-Tuning 🟡 │
│  Retrieval-Augmented  │       │  LoRA, QLoRA, RLHF,       │
│  Generation, vector   │       │  dataset prep             │
│  DBs, chunking        │       └───────────┬───────────────┘
└───────────┬───────────┘                   │
            └───────────────┬───────────────┘
                            │
                            ▼
┌──────────────────────────────────────────────────────────────┐
│  PHASE 5 · Agents                         🟡 Intermediate    │
│  Tool use, ReAct, memory, multi-agent systems                │
└───────────────────────────┬──────────────────────────────────┘
                            │
                            ▼
┌──────────────────────────────────────────────────────────────┐
│  PHASE 6 · Evaluation                     🟡 Intermediate    │
│  Evals, benchmarks, LLM-as-judge, eval pipelines            │
└───────────────────────────┬──────────────────────────────────┘
                            │
                            ▼
┌──────────────────────────────────────────────────────────────┐
│  PHASE 7 · Production                          🔴 Advanced   │
│  Deploy, scale, monitor, optimize, guardrails                │
└───────────────────────────┬──────────────────────────────────┘
                            │
                            ▼
┌──────────────────────────────────────────────────────────────┐
│  PHASE 8 · Advanced Topics                     🔴 Advanced   │
│  Train from scratch, multimodal, MoE, research papers        │
└──────────────────────────────────────────────────────────────┘

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🏁 Quick Start

Not sure where to begin? Pick your entry point:

🟢 "I know nothing about LLMs or ML"

Start → 00-foundations_what_are_llms.md

🟢 "I know ML / data science, new to LLMs"

Start → 02-prompt_engineering.md

🟢 "I'm a developer, just want to build an app NOW"

Start → 01-apis_quickstart.md

🟡 "I want to connect LLMs to my own data"

Start → 03-rag_pipeline.md

🟡 "I want to customize / fine-tune a model"

Start → 04-finetuning_lora.md

🔴 "I need to ship this to production"

Start → 06-safety_guardrails.md

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📚 Modules

Legend: 📖 Guide · 🧪 Notebook · 💻 Code · 🏗️ Project · 🟢 Beginner · 🟡 Intermediate · 🔴 Advanced

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🟢 Phase 0 — Foundations

No prerequisites. If you're brand new, start here.

#	Module	Format	Est. Time
0	What Are LLMs?	📖	20 min

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🟢 Phase 1 — Prompting

Learn to communicate with LLMs effectively. The highest ROI skill in this entire curriculum.

#	Module	Format	Est. Time
1	Prompt Engineering Techniques	📖 + examples	1 hr

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🟢 Phase 2 — APIs & Integrations

Call LLMs from your code. Build your first real application.

#	Module	Format	Est. Time
2	OpenAI API Quickstart	📖 + 💻	1 hr

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🟡 Phase 3 — RAG (Retrieval-Augmented Generation)

Give your LLM a memory. Connect it to your own documents and data.

#	Module	Format	Est. Time
3	Building a RAG Pipeline End-to-End	📖 + 🧪	2 hr

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🟡 Phase 4 — Fine-Tuning

When prompting isn't enough — adapt a model to your exact task.

#	Module	Format	Est. Time
4	LoRA and QLoRA: Efficient Fine-Tuning	📖 + 🧪	3 hr

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🔴 Phase 5 — Production

Ship it. Scale it. Keep it working. Don't go broke.

#	Module	Format	Est. Time
5	Safety, Guardrails & Content Filtering	📖 + 💻	1.5 hr

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📖 Additional Resources

Resource	Link
📚 Glossary	05-glossary.md

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🏗️ Projects Summary

Each phase ends with a capstone project you can actually put on your portfolio.

Phase	Project	Skills Demonstrated
0	Foundations	LLM basics
1	Prompt Playground	Prompt engineering
2	CLI Chatbot	API calls, conversation history
3	Chat with Your Docs	RAG, vector DB, embeddings
4	Custom Fine-Tune	Dataset prep, LoRA, model training
6	Safety Implementation	Guardrails, content filtering

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🗂️ Repository Structure

Practical-AI-engineering/
├── 00-foundations_what_are_llms.md
├── 01-apis_quickstart.md
├── 02-prompt_engineering.md
├── 03-rag_pipeline.md
├── 04-finetuning_lora.md
├── 05-glossary.md
├── 06-safety_guardrails.md
├── README.md                 ← you are here
├── contributing.md
├── roadmap.md
└── LICENSE

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🔖 Format Legend

Icon	Meaning
📖	Written guide (Markdown)
🧪	Jupyter Notebook (runnable)
💻	Standalone code / project
🏗️	Capstone project
🟢	Beginner — no prior ML knowledge needed
🟡	Intermediate — comfortable with Python and APIs
🔴	Advanced — ML background helpful

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🤝 Contributing

This curriculum is community-powered. All contributions welcome — big or small.

• Typo / small fix → open a PR directly
• Improve an explanation → open a PR with a brief note on what was unclear
• Add a new module → open an issue first so we can align on scope
• Translate to your language → see the Translations section

Please read contributing.md before submitting. Be kind.

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🌍 Translations

Language	Status	Maintainer
🇬🇧 English	✅ Complete	Core Team
🇮🇳 Hindi	🚧 In Progress	Contribute →
🇪🇸 Spanish	🚧 In Progress	Contribute →
🇵🇹 Portuguese	🙋 Needed	Contribute →
🇨🇳 Chinese (Simplified)	🙋 Needed	Contribute →
🇯🇵 Japanese	🙋 Needed	Contribute →

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💬 Community

• GitHub Discussions — questions, study groups, project showcases
• Issues — bug reports, content errors, suggestions

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📜 License

MIT — free to use, share, fork, and build on.
Attribution appreciated but not required.

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Built with ❤️ by the community · Contribute · Star it ⭐

This curriculum is updated regularly. Watch the repo to stay notified.