Image by Author
# The Theory of “Everything”
Data science projects rely heavily on foundational knowledge, be that organizational protocols, domain-specific standards, or complex mathematical libraries. Rather than scrambling across scattered folders, you should consider leveraging NotebookLM’s “second brain” possibilities. To do so, you could create an “everything” notebook to act as a centralized, searchable repository of all your domain knowledge.
The concept of the “everything” notebook is to move beyond simple file storage and into a true knowledge graph. By ingesting and linking diverse sources — from technical specifications to your own project ideas and reports to informal meeting notes — the large language model (LLM) powering NotebookLM can potentially uncover connections between seemingly disparate pieces of information. This synthesis capability transforms a simple static knowledge repository into a queryable robust knowledge base, reducing the cognitive load required to start or continue a complex project. The goal is having your entire professional memory instantly accessible and understandable.
Whatever knowledge content you would want to store in en “everything” notebook, the approach would 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 be loaded with core company documents, foundational research papers, internal documentation, and essential code library guides.
Crucially, this repository is not a one-time setup; it is a living document that grows with your projects. As you complete a new data science initiative, the final project report, key code snippets, and post-mortem analysis should be immediately ingested. Think of it as version control for your knowledge. Sources can include PDFs of scientific papers on deep learning, markdown files outlining API architecture, and even transcripts of technical presentations. The goal is to capture both the formal, published knowledge and the informal, tribal knowledge that often resides only in scattered emails or instant messages.
# Step 2. Maximize Source Capacity
NotebookLM can handle up to 50 sources per notebook, containing up to 25 million words in total. For data scientists working with immense documentation, a practical hack is to consolidate many smaller documents (like meeting notes or internal wikis) into 50 master Google Docs. Since each source can be up to 500,000 words long, this massively expands your capacity.
To execute this capacity hack efficiently, consider organizing your consolidated documents by domain or project phase. For instance, one master document could be “Project Management & Compliance Docs,” containing all regulatory guides, risk assessments, and sign-off sheets. Another could be “Technical Specifications & Code References,” containing documentation for critical libraries (e.g. NumPy, Pandas), internal coding standards, and model deployment guides.
This logical grouping not only maximizes the word count but also aids in focused searching and improves the LLM’s ability to contextualize your queries. For example, when asking about a model’s performance, the model can reference the “Technical Specifications” source for library details and the “Project Management” source for the deployment criteria.
# Step 3. Synthesize Disparate Data
With everything centralized, you can ask questions that connect scattered dots of information across different documents. For example, you can ask NotebookLM:
“Compare the methodological assumptions used in Project Alpha’s whitepaper against the compliance requirements outlined in the 2024 Regulatory Guide.”
This enables a synthesis that traditional file search cannot achieve, a synthesis that is the core competitive advantage of the “everything” notebook. A traditional search might find the whitepaper and the regulatory guide separately. NotebookLM, however, can perform cross-document reasoning.
For a data scientist, this is invaluable for tasks like 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) against the latency constraints documented in the Vector Database Performance Audit (Source C). Based on this synthesis, recommend an optimal chunking strategy that minimizes database retrieval time while maximizing the contextual relevance of retrieved chunks for the LLM.”
The result is not a list of links, but a coherent, cited analysis that saves hours of manual review and cross-referencing.
# Step 4. Enable Smarter Search
Use NotebookLM as a smarter version of CTRL + F. Instead of needing to recall exact keywords for a technical detail, you can describe the idea in natural language, and NotebookLM will surface the relevant answer with citations to the original document. This saves critical time when hunting down that one specific variable definition or complex equation that you wrote months ago.
This capability is especially useful when dealing with highly technical or mathematical content. Imagine trying to find a specific loss function you implemented, but you only remember its conceptual idea, not its name (e.g. “the function we used that penalizes large errors exponentially”). Instead of searching for keywords like “MSE” or “Huber,” you can ask:
“Find the section describing the cost function used in the sentiment analysis model that is robust to outliers.”
NotebookLM uses the semantic meaning of your query to locate the equation or explanation, which could be buried within a technical report or an appendix, and provides the cited passage. This shift from keyword-based retrieval to semantic retrieval dramatically improves efficiency.
# Step 5. Reap the Rewards
Enjoy the fruits of your labor by having a conversational interface sitting atop your domain knowledge. But the benefits don’t stop there.
All of NotebookLM’s functionality is available to your “everything” notebook, including video overviews, audio, document creation, and its power as a personal learning tool. Beyond mere retrieval, the “everything” notebook becomes a personalized tutor. You can ask it to generate quizzes or flashcards on a specific subset of the source material to test your recall of complex protocols or mathematical proofs.
Furthermore, it can explain complex concepts from your sources in simpler terms, summarizing pages of dense text into concise, actionable bulleted lists. The ability to generate a draft project summary or a quick technical memo based on all ingested data transforms time spent searching into time spent creating.
# Wrapping Up
The “everything” notebook is a potentially-transformative strategy for any data scientist looking to maximize productivity and ensure knowledge continuity. By centralizing, maximizing capacity, and leveraging the LLM for deep synthesis and smarter search, you transition from managing scattered files to mastering a consolidated, intelligent knowledge base. This single repository becomes the single source of truth for your projects, domain expertise, and company history.
Matthew Mayo (@mattmayo13) holds a master’s degree in computer science and a graduate diploma in data mining. As managing editor of KDnuggets & Statology, and contributing editor at Machine Learning Mastery, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.
