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A knowledge workspace about AI engineering in practice: what matters when building, integrating, and operating AI-powered systems on top of foundation models, and how they behave in production.
The repository contains concept definitions, example system analyses, speculative and emerging ideas, and threat descriptions. Notes are designed for quick reference and are cross-linked to form a navigable knowledge graph.
This repository focuses on AI engineering - the application layer of building with foundation models (prompt design, context engineering, tool orchestration, RAG, agents, evals, observability, and production concerns). ML engineering concepts (model training, data curation, architecture design) are referenced where relevant but not covered in depth.
The concepts/ folder contains a lightweight, definition-first glossary of AI engineering terms.
The threats/ folder contains notes on threats to AI systems, describing attack vectors and vulnerabilities.
The ideas/ folder contains notes on speculative, emerging, and opinion-driven ideas about AI systems, often attributed to specific external sources.
The example-systems/ folder contains analyses of concrete AI systems as compositions of concepts, describing how capabilities combine in real products and what trust model emerges from each specific composition.