Production AI Systems Documentation

Estimated time to read: 2 minutes

Welcome to the official documentation for the Production AI Systems roadmap. This project is a comprehensive guide and repository designed for Machine Learning and AI engineers transitioning from prototyping to deploying scalable, robust, and production-grade LLM applications.

🎯 Our Philosophy

"Frameworks change. Architecture remains."

Our goal is not to memorize high-level APIs or build generic wrappers. We focus on: - Systematic engineering: Robust error handling, strict typing, and comprehensive testing. - Deep optimization: Understanding tokenization math, embedding geometries, attention mechanisms, and retrieval economics. - Production reliability: Minimizing hallucinations, improving grounding, and designing failover-safe agent loops.

🧭 Documentation Map

Explore the different sections of the documentation to understand the architecture and project updates:

mindmap root((Production AI)) Foundations Tokenization Math Embedding Geometry Attention Blocks Architecture Design Principles ADRs Roadmap Progress Status Learning Path

🧠 LLM Foundations

Explore our experiments, starting with [LAB-01] Tokenization Math, which benchmarks BPE vs WordPiece, analyzes token compression ratios, and investigates the Portuguese token tax.

📐 Architecture & ADRs

Discover the core design principles guiding our implementations. Access our Architecture Decision Records (ADRs) to understand why specific structures and tools were adopted.

🗺️ Roadmap & Progress

Track the progression of the modules, difficulty mappings, and the status of active projects through the interactive Progress Log.