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# The “everything” theory
Data science projects rely heavily on background knowledge such as organizational protocols, domain-specific standards, and elaborate mathematical libraries. Instead of rummaging through scattered folders, you should consider using NotebookLM’s “second brain” capabilities. To do this, you can create an “everything” notebook that will act as a centralized, searchable repository of all knowledge about your domain.
The concept of an “everything” notebook is to go beyond elementary file storage and move to a true knowledge graph. By sourcing and combining sources ranging from technical specifications, to ideas for your own projects and reports, to notes from informal meetings, the large-language model (LLM) that underpins NotebookLM has the potential to uncover connections between seemingly disparate information. This ability to synthesize transforms elementary stagnant knowledge warehouse into questionable, solid knowledge resistreducing the cognitive load required to start or continue a elaborate project. The goal is to immediately access and understand your entire professional memory.
Regardless of the content of knowledge you would like to keep in your “everything” notebook, the approach will follow the same steps. Let’s take a closer look at this process.
# Step 1. Create a central repository
Designate one notebook as your “everything notebook.” This notebook should contain core company documents, basic research articles, internal documentation, and imperative code library guides.
Most importantly, this repository is not a one-time setup; it is a living document that grows with your projects. Once a modern data science initiative is completed, the final project report, key code snippets, and post-mortem analysis should be immediately adopted. Think of it as version control of your knowledge. Sources may include PDF files of research articles on deep learning, markdown files describing the API architecture, or even transcripts of technical presentations. The goal is to capture both formal, published knowledge and informal, tribal knowledge that is often found only in scattered emails or instant messages.
# Step 2. Maximize source capacity
NotebookLM can handle up to 50 sources on a notebook containing to 25 million words in fact. For data scientists working with huge documentation, a practical solution is to consolidate many smaller documents (such as meeting notes or internal wiki pages) into 50 main Google Docs. Because any source can be up to Length 500,000 wordsthis significantly increases your possibilities.
To successfully execute this capacity hack, consider organizing your consolidated documents by domain or project phase. For example, one master document might be “Project Management and Compliance Documentation”, which includes all regulatory guidelines, risk assessments, and signature sheets. Another might be “Technical Specifications and Code References,” which includes documentation for key libraries (e.g. NumPy, Pandas), internal coding standards, and model implementation guides.
This logical grouping not only maximizes word count, but also aids in targeted searches and improves LLM’s ability to contextualize queries. For example, when querying model performance, the model might refer to the “Technical Specifications” source for library details and the “Project Management” source for implementation criteria.
# Step 3. Synthesize various data
By centralizing everything, you can ask questions that connect scattered dots of information across documents. For example, you can ask NotebookLM:
“Compare the methodological assumptions used in the Project Alpha white paper with the compliance requirements set out in the 2024 Regulatory Guide.”
This enables synthesis that customary file searches cannot achieve. synthesis, which is the primary competitive advantage of the everything notebook.. A customary search may turn up a separate book and regulatory guide. However, NotebookLM can perform cross-document analysis.
As a data scientist, this is invaluable for tasks such as machine learning model optimization. You could ask something like:
“Compare the recommended chunk size and overlap settings for the text embedding model defined in the RAG System Architecture Guide (source A) with the latency constraints documented in the Vector Database Performance Audit (source C). Based on this synthesis, recommend an optimal sharding strategy that minimizes database retrieval time while maximizing the contextual relevance of retrieved chunks for LLM.”
The result is not a list of links, but consistent, cited analysis that saves you hours of manual review and cross-referencing.
# Step 4. Turn on smarter search
Apply NotebookLM as a smarter version CTRL + F. Instead of recalling exact keywords for technical details, you can describe your idea in natural language and NotebookLM will display the appropriate response along with citations to the original document. This saves you the time it takes to find a specific definition for a variable or elaborate equation you wrote months ago.
This feature is especially useful for highly technical or mathematical content. Imagine that you are trying to find a particular implemented loss function, but you only remember its conceptual idea and not its name (e.g. “we used a function that exponentially penalizes large errors”). Instead of searching for keywords like “MSE” or “Huber”, you can ask:
“Find the section that describes the cost function used in the sentiment analysis model that is robust to outliers.”
NotebookLM uses the semantic meaning of a query to locate an equation or explanation that may be hidden in a technical report or appendix and provides the quoted passage. This is a shift from keyword-based search to semantic retrieval dramatically improves performance.
# Step 5. Get rewards
Enjoy the fruits of your labor with a conversational interface that complements your domain knowledge. But the benefits don’t end there.
All NotebookLM functionality is available for your “everything” notebook, including video reviews, audio, document creation and its power as personal learning tool. Beyond elementary search, the “everything” notebook becomes a personalized teacher. You can ask it to generate quizzes or flashcards on a specific subset of the source material to test your memory of elaborate protocols or mathematical proofs.
What’s more, maybe explain elaborate concepts from sources in a simpler way, summarizing pages of dense text into concise, useful bulleted lists. The ability to generate a draft of a project summary or quick technical note from all the data you’ve retrieved transforms time spent searching into time spent creating.
# Summary
An “everything” notebook is a potentially transformative strategy for any data scientist looking to maximize productivity and ensure continuity of knowledge. By centralizing, maximizing capacity, and leveraging LLM for deep synthesis and smarter search, you move from managing distributed files to mastering a consolidated, clever knowledge base. This single repository becomes the single source of truth for your projects, domain expertise, and company history.
Matthew Mayo (@mattmayo13) has a master’s degree in computer science and a university diploma in data mining. As editor-in-chief of KDnuggets & Statologyand contributing editor at Machine learning masteryMatthew’s goal is to make elaborate data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and emerging artificial intelligence discovery. It is driven by the mission of democratizing knowledge in the data science environment. Matthew has been coding since he was 6 years venerable.