An agent is useful exactly as far as it sees the right context, and Devin gathers it by several paths at once: from the current and other open files, from a local index of the whole repository, and from retrieval that pulls relevant fragments as you work. For Teams and Enterprise, an index of remote repositories and a Knowledge Base from Google Docs are added to this.

The naive move, when context matters, is to add more of it: pin every document that seems related, open more files, connect the whole knowledge base. The logic is clear: the more the model sees, the smarter the answer. In practice this logic inverts past a certain threshold.

It breaks against competition for attention. The context window and the model's attention are finite; an extra pinned document adds no knowledge but dilutes it, pulling weight away from what actually pertains to the task. An overfilled context gives answers that are more generic and less precise - the exact opposite of what it was filled for.

The professional technique is to pin narrowly and to the point. Keep module definitions, the target framework, the files you are editing in context - and remove everything "that might come in handy". For external documentation, do not dump it whole but give the version, the date and the specific section: this both aims retrieval more precisely and guards against relying on a stale edition. Conversely, an empty context is as harmful as an overfilled one: a model that does not see the target framework or the neighboring module honestly fills the gaps with guesses, so the goal is neither a minimum nor a maximum but a precise selection for the task. Context is a map, not a warehouse: the selection is valuable, not the volume.

Knowing how the engine works under the hood helps build the right intuition. It is a retrieval-augmented approach: the local code is indexed in full, including unopened files, and a proprietary retrieval technique feeds the model relevant fragments. So the engine does not "remember the whole repository" verbatim but each time retrieves what resembles the query. Hence a practical consequence: the more precisely the task is stated and the more narrowly the context is pinned, the more precise the selection.

Team capabilities have their own mechanics and their own boundaries. Indexing remote repositories and the Knowledge Base are available on Teams and Enterprise; on Pro the context lengths and indexing limits are expanded. The Knowledge Base in the current beta accepts Google Docs as a shared source, understands tables, charts and formatted text, but does not import images from documents, and the maximum is about 50 documents. This is a snapshot guide, to be checked against the current page.