AI Engineer Vault

The Definitive Field Manual for Shipping Mission-Critical AI Systems

Why this repo?

• Architectural Depth: Moves beyond "how to call an API" to "how to build reliable systems."
• Production-Hardened: Focused exclusively on patterns that survive 24/7 enterprise traffic.
• Vendor Agnostic: Teaches the fundamental physics of LLMs, retrieval, and agents—not just specific SDKs.

Who this is for

• Target Audience: Competent software engineers, SREs, and architects moving from prototypes to production systems.
• Not for: Absolute beginners seeking basic tutorials or practitioners looking only for unannotated link lists.

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🏗️ Table of Contents

Core Curriculum

0. Foundations of AI Engineering
1. Prompt Engineering Patterns
2. RAG & Retrieval Systems
3. Agents, Tools & MCP
4. Evals & Testing
5. Production Ops & Reliability

Advanced Optimization

6. Cost Optimization
7. Fine-tuning & Model Adaptation
8. Multimodal Systems

Governance & Strategy

9. Security & Safety
10. Architecture Patterns
11. Research Frontiers

Reference & Resources

• Glossary of Terms
• Annotated Papers
• Vetted Tools
• Trusted Blogs & Newsletters

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Important

⭐ Star this repository to follow updates. We track daily shifts in the AI Engineering landscape to keep this manual at the cutting edge.

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📈 Quick Stats

Metric	Value
Chapters	12
Proven Patterns	50+
Annotated Papers	30+
Vetted Tools	40+
Target Reliability	99.9%

🤝 Contributing

We welcome contributions that meet our high architectural bar. Please read our CONTRIBUTING.md and STYLE_GUIDE.md before submitting a PR.

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© 2026 AI Engineer Vault. Licensed under MIT.