Claude Projects can automatically switch to RAG (retrieval augmented generation) as your knowledge library approaches the model’s context limit. Here is when that built-in setup is enough, and when a separate setup may help.
Brand and marketing teams often use Claude Projects as working folders for clients or campaigns. Projects can help organize brand assets, product notes, as well as store conversations for the team to reference over the course of the client engagement, without having to rebuild its context every single chat.
That setup works well for long-term projects that have lots of requirements, or even projects that you touch so infrequently that you can’t remember every minute detail.
What to keep in a Claude Project
Not every file needs to stay in a Project beyond one task. A client email or one-off draft may be useful at the time, while campaign briefs, writing guides, terminology notes, and product information are more likely to support work across several chats.
A Project becomes more useful when the same references support ongoing work. Keeping those files current and clearly organised makes the library easier to use as the workflow grows.
When Project knowledge fits within the available context window, Claude can use those files as working material for the conversation. The same window also has to hold the chat history, instructions, images, documents, tool results, and Claude’s response.
At Hire Digital, our Social Media Project contains more than a dozen files covering editing preferences, caption writing, post formats, research approaches, and a running post index. We update those files as the workflow changes, and an editor reviews every draft before publication.
The Instructions field sets the role and core rules that apply across the Project. For each new chat, we provide separate instructions, setting out the workflow and pointing Claude to the relevant guides, giving every task the same starting point without carrying over a long conversation.
The detailed guidance stays in the project Context files. Each one covers a specific type of work, uses a clear filename, and begins with a short note explaining when to use it. This makes the library easier to navigate and gives Search mode clearer material to retrieve once retrieval-augmented generation (RAG) is active.
When Claude starts searching the library
As a Project library approaches the model’s available context limit, Claude automatically switches from loading the full source set into context to retrieving relevant sections as needed. Anthropic says this automatic switch to RAG expands a Project’s capacity by up to 10 times. The context limit varies by model, while the amount of uploaded material determines when a Project reaches it.
Before that threshold, Claude can keep the full Project library in context. This makes it easier to draw on several files in one task. The downside is that the library shares the same context window as the conversation, instructions, tools, and response, which can fill quickly in our day-to-day use.
Because the Project library shares the context window, the format we choose can affect what Claude processes. We use plain text files (like .md, .txt or .csv) for simple content and raw data, but prefer their .docx/.xlsx versions for content mixed with formatting, images, tables, or comments. For everything else, we opt for PDF format.
These choices help keep the library small so we can make the most within a limited context. Once the library reaches the threshold, Claude changes how it uses those files. It stops loading the full set into context and retrieves only the sections it considers relevant. This lets the Project hold more material, but work across several documents depends on the search surfacing all the right sections.
Claude Projects or a RAG setup?
The choice depends on how often the source library changes, how the answers are used, and whether they need to be traced to a specific document.
- Keep Claude Projects when the team can keep the files current, an editor reviews the output, and built-in retrieval provides enough traceability.
- Consider a RAG setup when sources change frequently, answers are used without review, or each response needs to be traced back to a specific document.
What if I don’t use Claude?
Though we mostly discussed Claude, most other LLM providers have their own versions of Projects & RAG. For instance, ChatGPT Projects can draw from project chats, uploaded files, and custom instructions. Google’s Gemini Notes (Previously NotebookLM) lets users choose which sources are active and provides inline citations to the supporting material as well as allow for summaries in many formats.
Don’t spend too much time thinking about it
For Hire Digital, our current Claude Project supports social research, drafting, and editing without another system to maintain. With the addition of its internal “mini-RAG”, Claude Projects can handle most of the scenarios your team will face. Only if you find yourself hitting the limit on multiple occasions, a separate retrieval setup can be more appealing, but by then you’d be falling in a more niche category, especially as Claude and other frontier models continue to push their context limits.