An AI Workspace is most useful when work outgrows a single conversation. A thesis, client engagement, product launch, or policy library usually has recurring files, several workstreams, and questions that build on earlier decisions. Keeping all of that in one long chat makes context hard to find; scattering it across unrelated chats makes it easy to lose the source material.
In LLMBase, a Workspace gives related chats and ready files a shared project boundary. The goal is not to store everything. It is to make the material that should inform a project easy to find and reuse.
Create one Workspace per durable area of work
Name the Workspace after the work people are trying to finish, not after a vague department label. Good names make the scope obvious:
- “Master’s thesis — platform regulation, 2026”
- “Acme renewal — commercial and delivery review”
- “Product launch — customer research and positioning”
- “People policy library — approved internal guidance”
If a project has unrelated audiences, owners, or source material, create separate Workspaces. A clean boundary is more useful than one giant “Company AI” library because it tells every contributor what belongs there.
Add a small, curated source library
Start with the project brief, authoritative source documents, a glossary of terms, and any previous decision record that should shape future work. Give files descriptive names that include a date or version where relevant. A file called proposal-v3-2026-08-21.pdf is easier to review than final-final.pdf.
Wait until a file shows Ready before relying on it in a Workspace chat. A file still processing is not yet available as project context; a failed file should be retried or replaced before it becomes part of a conclusion.
Do not use a Workspace as a dumping ground. Remove superseded or irrelevant material so a person asking a question can understand which sources are current. Keep a short “read first” document when a project has a key decision, approved language, or definition that should guide every chat.
Separate workstreams with chats and folders
Use a new chat for each real piece of work: a research review, contract comparison, interview synthesis, launch copy, or meeting preparation. Folders can help group those chats by workstream without hiding the overall project boundary.
Open each prompt with the outcome and source scope. For example:
In this Workspace, compare the customer interview notes and the launch brief. Identify the three most common objections, quote the supporting evidence, and propose questions for the next round of interviews. Do not use material outside this Workspace.
That is better than “What do customers think?” because it tells the chat which material should count and what a useful answer looks like.
Create a lightweight operating rhythm
At the start of a project, make a short workspace note containing the purpose, owner, intended audience, source hierarchy, and review date. Update it when a decision changes. At the end of an important chat, ask for a concise decision record: what was decided, the evidence used, open questions, and next owner.
This habit makes the Workspace useful to the next person who opens it. It also prevents a good analysis from disappearing into an old transcript.
Use access and tools deliberately
Shared work needs clear ownership. Agree who may add or remove source material, which team members should see the project, and whether connected external tools are appropriate. Begin with the smallest scope that lets people do the work. When an external tool could change data, read the action carefully before approving it.
For sensitive projects, follow your organisation’s published policies and use the current legal and privacy materials that apply to your account. Do not assume that every file is appropriate to add simply because a Workspace exists.
A simple Workspace check before work begins
Before you ask a major question, verify five things:
- Is this the right Workspace for the task?
- Are the relevant files ready and current?
- Does the prompt name the decision and the sources to use?
- Is the requested output specific enough to review?
- Does a human still need to verify a source, calculation, or recommendation?
With those basics in place, a Workspace becomes a practical project memory: focused enough to be trustworthy, organised enough to be reusable, and flexible enough to support the next piece of work.
