Setting up context does not mean pasting everything your company knows into a prompt.
That is usually how teams create expensive noise.
Good context is curated, current, and connected to the task.
It tells the AI what it needs to know to do one kind of work well.
The best place to start is not "our whole business."
The best place to start is one repeated workflow.
Start with one workflow
Pick a workflow where the team already repeats the same explanation over and over.
Examples:
- Drafting customer responses
- Summarizing sales calls
- Reviewing product feedback
- Preparing weekly status updates
- Creating first drafts of policy explanations
- Turning research notes into executive summaries
- Reviewing pull requests or release notes
The workflow should be specific enough that people can agree on what good output looks like.
"Help with strategy" is too broad.
"Turn these meeting notes into a decision memo for the operating team" is better.
Define the job
Before collecting documents, define the job.
Ask:
- What should the AI produce?
- Who is the output for?
- What decision or action should it support?
- What does good look like?
- What should the AI never do?
- Where does human judgment enter?
This step matters because context depends on the job.
The context needed for a board memo is different from the context needed for a customer support draft, even if both involve the same business.
Gather source material
Once the job is clear, gather only the sources that help.
Useful context can include:
- Approved policies
- Product facts
- Customer promises
- Tone examples
- Prior good outputs
- Definitions
- Decision rules
- Escalation paths
- Known exceptions
- Things the AI should avoid
Do not confuse raw intake with memory.
A folder full of documents is not a context system. It is a pile.
Context becomes useful when the team decides which sources matter, how they should be used, and who keeps them current.
Write rules and examples
AI tools often perform better with examples.
If the team has three examples of good work, include them. If there are common bad outputs, name those too.
For example:
- Use a concise executive tone.
- Do not make policy promises.
- Flag missing information instead of guessing.
- If the customer asks for something outside policy, escalate.
- Return the answer in this format: issue, recommendation, risk, next action.
These rules reduce ambiguity.
They also make the workflow easier to review because everyone knows the standard.
Decide what not to include
Context design is also subtraction.
Do not include stale policies. Do not include random chat history. Do not include documents just because they are available. Do not include sensitive data unless the workflow truly requires it and the tool is approved for that use.
Every extra piece of context has a cost.
It can increase token usage, slow the system down, confuse the model, and expand the privacy or security surface.
The question is:
Does this context improve the output enough to justify including it?
Create an update habit
Context decays.
Policies change. Products change. Customers change. Definitions change. Teams learn better examples. Old instructions become wrong.
A context setup needs an owner.
Someone should know where the source material lives, when it was last reviewed, who can update it, and how changes are tested.
This does not need to be heavy bureaucracy.
It does need to be explicit.
The leader's starting checklist
To start setting up context, ask the team to define:
- One repeated workflow
- The intended output
- The user or reader
- The required source material
- The output format
- The review step
- The examples of good work
- The list of excluded or sensitive information
- The owner for updates
That is enough to begin.
Do not start by building a giant AI knowledge system.
Start by making one workflow less vague.