Context Building Is the Underrated AI Habit
While we are all prompting away with AI chatbots, one positive habit is emerging: context building.
Good prompting starts with giving the full picture.
That usually means pulling together the emails, docs, chats, meeting notes, and everything else that matters.
In the process, you are forced to slow down, reflect, and reconstruct the situation.
That alone is powerful.
The Prompt Is Not The First Step
The visible work is the prompt. The useful work usually happens before it.
The first artifact is not the generated email, memo, or deck. It is the reconstructed situation the model needs before it can help.
When someone asks an AI system to write the next email, summarize an account, help with a strategy memo, or prepare a decision, the model needs more than the immediate request. It needs the background: what happened, who is involved, what changed, what constraints exist, what has already been tried, and what outcome matters.
That preparation changes the user as much as it changes the model output.
Before AI, many of us would jump straight into the email, memo, deck, or decision. Now the better pattern is to first build the context window.
| Old reflex | Better AI-era habit |
|---|---|
| Start writing immediately. | Reconstruct the situation first. |
| Ask for a generic output. | Give the model the history, constraints, and goal. |
| Treat context as overhead. | Treat context as the work surface. |
Context Building Feels Like Journaling
There is something quietly useful about gathering scattered inputs and turning them into a coherent narrative.
You are not just feeding the model. You are organizing your own thinking.
That is why this often feels like journaling. You take a messy situation and make it legible. You capture the facts, the assumptions, the emotional charge, the missing details, and the next decision.
There is a structured version of this in cognitive behavioral therapy: capture the situation, the thoughts, the evidence, and then reframe.
The same pattern shows up in writing. A good screenwriter does not start with random dialogue. They build the character, the backstory, the motivation, and the scene. Only then does the story make sense. Only then does the next line feel right.
Knowledge work is starting to work the same way.
The Context Window Becomes A Work Surface
The context window is more than a technical limit. It is becoming a work surface.
For a GTM team, context might include:
- The account history.
- The current opportunity stage.
- Relevant emails and call notes.
- The buyer's role and likely priorities.
- The company's recent funding, hiring, product launches, or market events.
- The ICP, fit, eligibility, and persona rules that should guide the work.
- The sender's style, constraints, and relationship to the account.
Once that context is assembled, the AI output becomes less generic. More importantly, the human has a better mental model of the situation.
The human leaves with a cleaner situation model, not just a better generated artifact.
One Chat Per Topic Is Often Better
I have found it useful to keep one chat per topic, with all the context, and keep it alive for the life of the deal, project, or opportunity.
When a chat gets too long, it can lose the thread and miss important detail from earlier context. The better pattern is to treat long conversations as working folders, not infinite streams. Keep the important context together, but periodically compress it into a clean brief.
That creates a reusable asset:
- What is true?
- What changed?
- What still needs to be decided?
- What does the agent need to know before it acts?
Treat long-running chats like working folders. Keep the source context nearby, then periodically compress it into a clean brief.
This is where AI chat becomes more than a writing assistant. It becomes a way to maintain a living situation model.
The Habit Matters Even Without AI
AI or not, the discipline of gathering context first, then thinking, then writing is a very good habit.
It slows down reactive work. It makes hidden assumptions visible. It gives teams a shared version of the situation. It reduces the chance that the next action is based on a half-remembered thread or a stale CRM field.
For GTM teams, that matters because the expensive mistakes are usually context mistakes:
- Reaching out with the wrong premise.
- Treating a qualified account like a generic lead.
- Missing a buying signal.
- Forgetting the prior objection.
- Asking the rep or agent to act before the situation is understood.
Most bad AI-assisted GTM work is not caused by weak prose. It is caused by asking the system to act from stale, missing, or generic context.
The AI era may make context building more important, not less.
The best operators will not just write better prompts. They will build better context.