Counterintuitively, the goal of Claude's `.clodmd` files is not to load maximum data, but to create lean indexes. This guides the AI agent to load only the most relevant context for a query, preserving its limited "thinking room" and preventing overload.
The detailed plans co-created with an AI agent are valuable assets. Store these plan files in your team repository alongside final documents. This creates a library of reusable workflows that saves time and institutionalizes knowledge for future complex tasks.
Manage collective team context—docs, queries, research—in a version-controlled repository. Everyone, including non-technical members like ops and strategy, contributes via pull requests, creating a single, evolving source of truth for AI agents and humans.
Instead of forcing an AI to read lengthy raw documents, create consistently formatted summaries. This allows the agent to quickly parse and synthesize information from numerous sources without hitting context limits, dramatically improving performance for complex analysis tasks.
Empower your entire team to perform data analysis safely by having analysts check verified SQL queries, table schemas, and analysis playbooks into a shared repository. This reduces reliance on the data team and prevents incorrect, "hallucinated" results from AI agents.
The biggest lever for mastering AI is creating time to learn. Instead of trying to learn everything at once, focus on using AI to automate one recurring task. Reframe the goal not as pure efficiency, but as a strategic investment in time for experimentation and upskilling.
A shared AI knowledge repository ("Team OS") is not just for technical roles. Partners in business operations, strategy, and other non-technical functions are active daily contributors via GitHub, adding their context and making the system more powerful for everyone.
Go beyond simple instruction. Explicitly prompt your AI to use tools like `ask_user_question` to push your thinking, question your goals, and suggest alternative angles. This transforms the AI from a simple executor into a powerful strategic thinking partner.
Don't just use AI tools; ask them to explain *why* they work. Prompt the AI to break down concepts (e.g., repository structure) and to critique your own setup against best practices. This metacognitive loop accelerates learning and continuous improvement.
Instead of basic prompting, use an AI agent's "plan mode" to collaboratively outline a complex task, like writing a strategy doc. This lets you align on structure, sources, and verification steps before generation, yielding far superior results. It's like briefing a junior employee.