Federico Donatone’s Strategy for Managing Multiple AI Chats Efficiently

In a recent LinkedIn post, Federico Donatone shares a novel approach to managing numerous AI chatbot interactions, demonstrating how to streamline complex workflows by treating the AI as a subordinate manager. Donatone outlines a system designed to maximize productivity when working with tools like Claude, emphasizing delegation and structured communication.

The Core of Donatone’s System: Delegation and Structure

Federico Donatone’s strategy hinges on a principle of indirect management, drawing a parallel to running a company. He argues that directly engaging with every individual task or query, whether from employees or AI, is inefficient. Instead, he advocates for building an intermediary layer – a ‘manager’ AI – that handles the bulk of the work and presents a consolidated output.

“I give it one /goal per task. It opens a new Claude chat for each task. It writes a custom brief for every chat. It reviews every answer before I see it. It returns one final answer to me.”

This systematic approach, as detailed by Donatone, involves setting clear objectives for each AI chat, ensuring each new task initiates a dedicated conversation. The AI is then tasked with creating a specific brief for that interaction, reviewing the responses it generates, and finally presenting a single, curated answer to Donatone. This process transforms a potentially chaotic stream of information into a manageable dialogue.

Achieving Exponential Productivity Gains

Donatone claims significant results from implementing this method. He states that by adopting this ‘manager’ AI model, he has achieved more in the past 30 days than in the preceding six months. This dramatic increase in output underscores the power of effective AI delegation.

The ‘Manager’ AI Concept

The central tenet of Donatone’s advice is to build and interact with an AI ‘manager’ rather than engaging directly with every AI instance. He elaborates on this by saying:

“You wouldn’t run a company by answering to every employee yourself. Same rule here. Build the manager. Then talk only to it.”

This analogy highlights the importance of abstraction and hierarchical communication in managing complex AI operations. By designating one AI instance to act as a supervisor and filter, users can maintain clarity and focus, avoiding the cognitive overload associated with managing multiple, independent AI dialogues simultaneously.

Practical Application and Future Implications

Donatone’s practical advice offers a clear blueprint for professionals leveraging AI tools. The emphasis on defining goals, creating briefs, and reviewing outputs before final delivery provides a robust framework for anyone looking to enhance their productivity with AI. As AI becomes more integrated into daily workflows, strategies like Donatone’s will likely become increasingly crucial for harnessing its full potential without succumbing to its complexities.