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.

Don't try to create a comprehensive "memory" for your AI in one sitting. Instead, adopt a simple rule: whenever you find yourself explaining context to the AI, stop and immediately have it capture that information in a permanent context file. This makes personalization far more manageable.

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.

The effectiveness of AI tools like ChatGPT depends entirely on the quality of the initial inputs. To get exceptional results, "brief" the AI by uploading foundational documents like your company manifesto, jobs-to-be-done, and brand positioning. A lazy or generic prompt yields generic results.

Instead of holding context for multiple projects in their heads, PMs create separate, fully-loaded AI agents (in Claude or ChatGPT) for each initiative. These "brains" are fed with all relevant files and instructions, allowing the PM to instantly get up to speed and work more efficiently.

By creating a private GPT and uploading all relevant business information, you can build an AI consultant that understands your specific context. This helps clarify priorities, vet new strategies, and maintain focus.

Building a comprehensive context library can be daunting. A simple and effective hack is to end each work session by asking the AI, "What did you learn today that we should document?" The AI can then self-generate the necessary context files, iteratively building its own knowledge base.

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.