The first weeks of a new role create a flood of new context and a queue of basic questions. I turned that context into an AI mentor I could ask at any time. It answers from my meeting notes and the important decks I encountered, so I can ask the questions I would hesitate to repeat around colleagues.
The first weeks of a new role are a context problem
In week one, I needed to get fluent in a new domain quickly. The context sat across meetings, decks, and documents. A question about an acronym could lead to forty slides. A question about an old decision could lead to three people and a meeting recording.
The obvious questions often felt too basic to keep asking a busy colleague. So I stayed quiet, nodded along, and spent longer finding the answer alone.
The job was simple: keep the work context in one place and make it available whenever I had a question.
Why the usual ramp-up advice only gets you halfway
Most ramp-up advice focuses on people: find a mentor, ask good questions, meet stakeholders, read the documentation. All of that helps. The limits show up in the gaps between those moments.
A human mentor brings judgment and has limited time. Asking "explain this like I joined yesterday" for the tenth time at 11 PM is rarely realistic.
A generic AI chat has endless patience and generic industry knowledge. Your team's decision from last Tuesday, and the reason behind it, sit outside its context.
The company's documentation holds the context across decks and meeting notes. Finding one answer becomes a research project each time.
I needed my work context, endless patience, and instant answers in the same place.
The setup: one running document, fed automatically
A simple automation appends the notes from every meeting to one running document. Each meeting adds another layer of vocabulary, decisions, history, and names. The value grows because the relevant context stays together.
The setup has three parts:
1. Capture. Every meeting produces notes.
2. Accumulation. A simple automation appends those notes to one running document.
3. Grounding. The running document and the important decks go into NotebookLM.
This required almost no code. The useful work was choosing what context mattered and connecting tools that already existed.
Turning the document into answers with NotebookLM
I loaded the running notes document and the important decks I encountered into NotebookLM. That gave it the source material for my role instead of generic internet context.
Then I kept asking questions. Each answer stayed grounded in the meetings and decks I had supplied. As the running document grew, the answers improved through the ramp.
The workflow was simple:
1. Automate the meeting notes into one running document.
2. Add that document and the important decks to NotebookLM.
3. Keep asking questions as the context grows.
The tools can change. The accumulated context is the durable part. Any grounded Q&A tool needs useful source material before it can become useful itself.
What I actually ask it
The best questions are usually the ones I would swallow in a meeting:
"What does this acronym mean here?"
"Why did we choose this approach?"
"Explain this decision like I joined yesterday."
"Who owns this part, and what decisions have already been made?"
Daily use felt natural. A meeting ended, the document grew, and the next question used everything that came before it. The system was always available, patient, and free of judgment. Those qualities made the basic questions easier to ask.
It made me feel less lost within the first few weeks. Months later, the setup was still running, and the tools had improved around the habit.
Where the AI mentor helps, and where the human mentor still wins
It handles the context layer: vocabulary, decisions, history, who decided what, and why. It clears the basic questions quickly and lets human conversations start at a higher floor.
Human mentors still own judgment, politics, and career questions. They read people, incentives, and the unsaid parts of a situation. That kind of guidance depends on lived experience and trust.
The two work well together. NotebookLM gives me the background. A person helps me decide what the background means.
How it naturally closes
This system has a natural end. Once you are ramped up and deep in the work, the basic questions become less frequent. The context is already in your head, and the daily habit tapers off. By then, the system has done its job.
The build is simple. It keeps context alive after each meeting and gives basic questions somewhere to go immediately. Understanding builds quietly, at your own pace.
Start with your next meeting. Let a simple automation append the notes to one running document. Add that document and the important decks you encounter to NotebookLM. Then ask the question you have been sitting on since week one.