To elevate AI performance, create a structured folder system it can reference. This 'operating system' should include folders for persistent knowledge (e.g., `/knowledge`, `/people`), and active work (`/projects`). Providing this rich, organized context allows the AI to generate highly relevant, non-generic outputs.

While `claude.md` files can guide AI behavior, they aren't always adhered to. Use Claude Code's "session start hooks" instead. They guarantee that critical context like goals, tasks, and past mistakes is injected into every new chat, making the AI more reliable.

Structure AI context into three layers: a short global file for universal preferences, project-specific files for domain rules, and an indexed library of modular context files (e.g., business details) that the AI only loads when relevant, preventing context window bloat.

Claude Code's terminal-based interaction within a specific folder allows it to automatically read and reference local files. This makes "context engineering" drastically faster and more powerful than manually pasting information into a traditional chat interface, as the context is implicitly understood.

A disciplined folder structure (`Context`, `Projects`, `Templates`, `Tools`, `Temp`) is crucial for effective Claude Code use. It helps you stay organized and enables the AI to easily find relevant information, making it a more personalized and powerful assistant.

The easiest way to teach Claude Code is to instruct it: "Don't make this mistake again; add this to `claude.md`." Since this file is always included in the prompt context, it acts as a permanent, evolving set of instructions and guardrails for the AI.

Treat a simple folder on your computer as a "project" in Cowork. This folder, containing context files like a "brain.md," becomes a persistent and transferable memory hub, ensuring the AI always has the right context without starting from scratch on new tasks.

Frame your personal and professional information as a structured set of machine-readable files. This "operating manual" allows AI agents to understand your roles, goals, and constraints without constant re-explanation, just as a developer uses API docs to interact with software.

Instead of explicitly telling an AI agent how to organize its knowledge, simply provide the necessary context. A well-designed agent can figure out what information is important and create its own knowledge files, such as a 'user.md' for personal details or an 'identity.md' for its own persona.