To get highly specialized AI outputs, use ChatGPT's "projects" feature to create separate folders for each business initiative (e.g., ad campaign, investment analysis). Uploading all relevant documents ensures every chat builds upon a compounding base of context, making responses progressively more accurate for that specific task.

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.

Instead of one large context file, create a library of small, specific files (e.g., for different products or writing styles). An index file then guides the LLM to load only the relevant documents for a given task, improving accuracy, reducing noise, and allowing for 'lazy' prompting.

Long, continuous AI chat threads degrade output quality as the context window fills up, making it harder for the model to recall early details. To maintain high-quality results, treat each discrete feature or task as a new chat, ensuring the agent has a clean, focused context for each job.

Instead of overloading the context window, encapsulate deep domain knowledge into "skill" files. Claude Code can then intelligently pull in this information "just-in-time" when it needs to perform a specific task, like following a complex architectural pattern.

Treat Skills as permanent, reusable team members (e.g., a "copywriter"). Use Projects for context-specific, temporary initiatives with a clear start and end, like a seasonal marketing campaign. This mental model clarifies when to use each feature.

Treat AI 'skills' as Standard Operating Procedures (SOPs) for your agent. By packaging a multi-step process, like creating a custom proposal, into a '.skill' file, you can simply invoke its name in the future. This lets the agent execute the entire workflow without needing repeated instructions.

Run separate instances of your AI assistant from different project directories. Each directory contains a configuration file providing specific context, rules, and style guides for that domain (e.g., writing vs. task management), creating specialized, expert assistants.

Notion's team uses a `claude.md` file in their repo root to provide global instructions (e.g., tech stack) to their AI assistant. A git-ignored `claude.local.md` file is then used by each developer to provide personal context, like their username, which prevents the AI from modifying others' work.