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leadership cohort notebook Pn

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What you will learn

Every time you start a new AI chat, it knows nothing about you. This section shows you how to fix that — building a knowledge base that gives AI the context it needs to produce relevant, specific output from the first message.

Why context matters

Understand why AI outputs are generic by default and what changes when you give it your world

NotebookLM

Use Google’s NotebookLM to create a queryable knowledge base from your own documents

Memory and instructions

Use built-in memory and custom instructions to carry context across every conversation

What to upload

Know which documents belong in a knowledge base and which governance considerations apply

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Why this matters to you

You have used AI in this session with a scenario and context pasted directly into the prompt. That works. But it requires you to re-explain your business, your priorities, and your context every single time you start a new conversation. A knowledge base removes that friction. Instead of briefing AI from scratch, you upload your documents once. AI draws on them every time you ask a question. The output stops being generic advice and starts being analysis grounded in your actual business.
Think of building a knowledge base as onboarding a new team member. You would not expect a new hire to give useful input on day one without any background. You would give them the strategy doc, the key client briefs, the recent financials, and the context they need to contribute. A knowledge base does the same thing for AI — permanently.

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What a knowledge base adds

AI tools like ChatGPT already carry some context about you if you have set up custom instructions or memory. A knowledge base takes that further. Instead of remembering your preferences, AI can now reference your actual documents, your strategy, and your organisation’s specific context in every response.

Without a knowledge base

Responses draw on general knowledge. Analysis applies to your sector, not your organisation. You find yourself re-explaining background that does not change, your client base, your competitive position, your priorities, each time you start a new task.

With a knowledge base

Responses reference your actual documents. Analysis is grounded in your strategy and context. The starting point is already calibrated to your organisation. Less time briefing, more time using the output.

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NotebookLM: a knowledge base you can query

Google NotebookLM is purpose-built for this. You upload documents — strategy papers, client briefs, research reports, board presentations, meeting notes — and it creates a knowledge base you can query in natural language. The key difference from standard AI chat is that NotebookLM only draws on what you have uploaded. It does not blend your documents with general internet knowledge.
NotebookLM does have a Discover feature that can search the internet, but this is not part of the standard setup. In most cases, and for everything covered in this session, it works purely from what you have uploaded.
1

Create a new notebook

Open NotebookLM and create a notebook for the project or context you are working on. You can have multiple notebooks — one for a specific deal, one for your business strategy, one for a client account.
2

Upload your source documents

Add the documents relevant to this notebook. Strategy docs, research outputs, client briefs, competitor reports, meeting notes. NotebookLM accepts PDFs, Google Docs, and pasted text. The more relevant the sources, the more useful the outputs.
3

Ask questions in natural language

Query the notebook as you would ask a well-briefed colleague. “What are the three biggest risks across these documents?” or “What does our strategy say about entering new markets?” or “Find any contradictions between the briefing and the research report.”
4

Use the Audio Overview

NotebookLM can generate an Audio Overview — a conversational summary of your documents in podcast format. Use this to get across a large volume of material quickly, or to share context with a colleague who does not have time to read the source documents.
5

Build on the output

Use the knowledge base output as an input for your Thinker, Assistant, or Creator prompts. The knowledge base surfaces what is in your documents. The three roles turn it into something you can act on.

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Carrying context across conversations

NotebookLM is a dedicated tool for document-based knowledge bases. But most AI tools have built-in ways to carry context across conversations, so AI knows something about you before you say a word. The three most common are custom instructions, memory, and dedicated project spaces.
Most AI tools let you set standing instructions that apply to every conversation. Use these to tell AI who you are, what you do, how you want responses formatted, and what context it should always keep in mind.
Example: "I am the CEO of a professional services firm with 200 staff. I work fast and need direct, concise responses. Never use bullet points unless I ask. Always flag risks explicitly."
These instructions persist across every new chat. You do not need to re-explain your context each time. Available in ChatGPT, Claude, Copilot, and Gemini.
Some AI tools store things they learn about you across conversations. You can also manually tell them to remember something: “Remember that our key client is in the aged care sector and we are currently in a competitive pitch process.”Review your saved memories periodically and remove anything outdated. Memory is useful but not infallible — verify that what has been stored is still accurate. Available in ChatGPT and Claude.
Several AI tools let you create a persistent workspace where you upload documents and set custom instructions. Every conversation within that space starts with that context already loaded. Use this for ongoing work where you want AI to always have background on the topic. Available in ChatGPT (Projects), Claude (Projects), and Copilot (Notebooks).

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What to put in your knowledge base

Not every document belongs in a knowledge base. The most useful inputs are documents that would help a well-briefed analyst answer questions about your business, your clients, or your strategic priorities.

High-value inputs

Strategy and planning documents, client briefs and account summaries, research reports and market analysis, board presentations and investment theses, project briefs and scopes of work, recent meeting notes with decisions and actions

Lower-value inputs

Documents with highly sensitive personal data, outdated materials that no longer reflect current strategy, one-off communications that do not contain reusable context, anything containing information that should not be shared beyond its original recipients

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Try this now

Think about the work you’re doing this week. What’s one project or ongoing area where AI could deliver much better results if it actually understood your context? Pick three to five documents that would give AI the background it needs. Upload them to a NotebookLM notebook or paste the key context into your AI tool’s memory or custom instructions. Then ask it a question you would normally ask a well-briefed colleague. Notice what changes.

Workflow Discovery Tool

Use this tool to map which parts of your ongoing workflows would benefit most from persistent AI context — and identify the first documents worth uploading.
A knowledge base is the foundation of every AI agent you will eventually build. Agents do not operate in a vacuum — they draw on context about your business, your clients, and your processes to make decisions and produce relevant outputs. Building your knowledge base now means you are not starting from scratch when you get to agent-building. You are giving your future agents the same context you are giving AI today.

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Quick checkpoint

You are done with this section when you can:

Create a notebook

Set up a NotebookLM notebook and upload at least three relevant documents

Query your knowledge base

Ask a question you would normally ask a colleague and compare the output to a standard AI chat response

Set persistent context

Configure custom instructions or memory in your primary AI tool so it knows your role and context

Apply governance

Know which documents are appropriate to upload based on your organisation’s AI policy and your plan’s data handling terms