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Unlike standard AI chats which are isolated, Cowork's "Projects" feature allows you to chain multiple tasks together. All tasks within a project share the same context and memory, allowing the AI to build on previous work and understand the larger goal.
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.
Go beyond single-chat prompting by using features like Claude's "Projects." This bakes in context like brand guidelines and SOPs, creating an AI "second brain" that acts as a strategic partner, eliminating the need to start from scratch with each new task.
Cowork's interface for managing multiple tasks within a project allows any user to act as an "AI orchestrator." You get a high-level dashboard to run many agents at once, see which ones need attention, and grant permissions, much like a developer managing microservices.
Most AI tools are single-player experiences. Linear is designing its agent sessions to be shared, collaborative spaces. Multiple people, like a PM and a designer, can jump into the same chat with an agent, see its work, and give it feedback together, collapsing the collaboration loop.
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.
The next frontier for AI isn't just personal assistants but "teammates" that understand an entire team's dynamics, projects, and shared data. This shifts the focus from single-user interactions to collaborative intelligence by building a knowledge graph connecting people and their work.
Unlike ChatGPT's Custom GPTs which often "forget" past interactions, Claude's "Projects" feature builds a persistent memory. It learns from all previous threads within a project, layering that knowledge on top of initial instructions to improve its output over time.
The greatest leverage from AI comes not from accelerating individual tasks, but from improving information flow between teams. Use AI to create a "common brain"—a central repository of project knowledge and goals—to ensure alignment and drive efficiency at critical handoff points.
Non-technical users are accustomed to a "prompt, wait, respond" cycle. Cowork's design encourages a new paradigm where users "hand off" significant work, let the AI run for hours, and check back on results, much like delegating to a human assistant.
AI has no memory between tasks. Effective users create a comprehensive "context library" about their business. Before each task, they "onboard" the AI by feeding it this library, giving it years of business knowledge in seconds to produce superior, context-aware results instead of generic outputs.