The team explains the business to AI, gets a good deliverable and has to repeat the explanation on the next task. Gathering the materials in a project gives continuity, but it creates a responsibility: keeping the context for AI understandable and up to date.
In Claude's Projects, for example, documents and instructions make up the working context. How well that is organized depends on human decisions about what is current, what is history and who corrects a reference.
Organize the base by purpose
A team that prepares proposals gathers service descriptions, past examples and terms of service. If everything goes in without labels, an old proposal can guide the writing as if it were still the current offer.
The base should tell apart three things: current information, style examples and history. A document can be a reference for language and still be the wrong source for price or scope. A short note on each file's role, who updates it and when it was last reviewed reduces that ambiguity. The decision about what is valid has to exist outside the AI's interpretation.
Test the context with a question you can check
Ask for a summary of the current terms with the sources used, and compare it with what the team considers valid. If there is a conflict, clarify which information takes precedence before expanding use. The test may reveal an operational problem: two teams working with different versions of the same rule.
Agree on when the base will be reviewed. A change in the offer should reach the instructions before the next proposal.
Looking after context is an editorial and operational responsibility. In AI Thinking by Dani Hayoshi, it is part of deciding who does each step — including the step of keeping the right information. See also How to use AI at work.
Dani Hayoshi is the author of AI Thinking. Her work connects artificial intelligence, education and entrepreneurship. About Dani.