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# The “everything” theory
Data science projects rely heavily on foundational knowledge, be it organizational protocols, domain-specific standards, or complex mathematical libraries. Instead of rummaging through scattered folders, you should consider taking advantage of NotebookLM’s “second brain” possibilities. To do this, you can create an “everything” notebook to serve as a central, 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 consuming and combining diverse sources – from technical specifications to your own project ideas and reports to informal meeting notes – the Large Language Model (LLM) Powering Notebook LM can potentially uncover connections between different pieces of information. This synthesis ability replaces a simple static knowledge storage In a questionable strong knowledge The basisreducing the cognitive load required to start or continue a complex project. The goal is to make your entire professional memory instantly recognizable and understandable.
Whatever knowledge content you want to keep in the “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 be filled with basic company documents, basic research papers, internal documents, and the essential code library manual.
Importantly, this repository is not a one-time setup. It’s a living document that grows with your projects. When you complete a new data science initiative, the final project report, key code snippets, and post-mortem analysis should be entered immediately. Think of it as version control for your knowledge. Sources can include PDFs of scientific papers on deep learning, Markdown files outlining API architectures, and even transcripts of technical presentations. It aims to capture both formal, published knowledge and informal, tribal knowledge that often resides only in scattered emails or instant messages.
# Step 2. Maximize the source capacity
The notebook can handle up to LM 50 sources per notebook, which includes 25 million words For data scientists working with vast amounts of documents today, a practical hack is to consolidate many small documents (such as notes or an internal wiki) 50 Master Google Docs. Since each source can be 500,000 words longit expands your potential massively.
To effectively implement this capability hack, consider organizing your static documents by domain or project phase. For example, a master document could be the “Project Management and Compliance Documents”, which includes all regulatory guides, risk assessments and sign-off sheets. The second might be “Technical Specifications and Code References”, which includes documentation for critical libraries (such as Nami, Pandas), internal coding standards, and model deployment guides.
This logical grouping not only maximizes word count, but also helps focus searches and improves LLM’s ability to contextualize your questions. For example, when asking about the performance of a model, the model might refer to the “Technical Specifications” source for the model library specification and the “Project Management” source for the deployment criteria.
# Step 3. Synthesize the different data
With Centralized Everything, you can ask questions that connect the scattered dots of information in different documents. For example, you can ask NotebookLM to:
“Compare the methodological assumptions used in Project Alpha’s white paper against the compliance requirements outlined in the 2024 Regulatory Guide.”
This enables a combination that traditional file searches cannot achieve, A recipe that is the main competitive advantage of “everything” notebooks. A traditional search can find the white paper and the regulatory guide separately. However, Notebook LM can perform cross-document reasoning.
For a data scientist, this is invaluable for tasks such as machine learning model optimization. You can ask something like:
“Compare the chunk sizes and overlap settings recommended for the text embedding model described 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, an optimal chunking strategy is recommended.
The result is not a list of links, but an integrated, referenced analysis that saves hours of manual review and cross-referencing.
# Step 4. Enable Smart Search
Use Notebook LM as an enhanced version ctrl + f. Instead of needing to memorize the exact keywords for a technical description, you can describe the idea in natural language, and Notebook LM will highlight the relevant answer level with references to the original document. This saves significant time when struggling with a specific variable definition or complex equation that you wrote months ago.
This ability is especially useful when dealing with highly technical or mathematical material. Imagine that you tried to find a specific loss function, but you only remember its theoretical idea, not its name (like “the function we use that quickly punishes large errors”). Instead of searching for keywords like “mse” or “huber” you can ask:
“Find the part that describes the cost function in a sentiment analysis model that is robust to outliers.”
Notebook LM uses the semantics of your query to find an equation or explanation, which may be buried in a technical report or appendix, and provides a cited reference. This changes from keyword-based retrieval Semantic recovery Dramatically improves performance.
# Step 5. Reap the rewards
Enjoy the fruits of your labor by having a conversational interface sitting on top of your domain knowledge. But the benefits don’t stop there.
All the functionality of Notebook LM is available for your “everything” notebook, including video review, audio, document creation, and its power as A personal learning tool. Beyond mere retrieval, the “everything” notebook becomes a personal tutor. You can ask him to prepare quizzes or flashcards on a specific subset of the source material to test his recall of complex protocols or mathematical proofs.
Moreover, it can Explain complex concepts Turning pages of dense text into concise, actionable bullet lists from your sources into simple words. The ability to create a draft project summary or create a quick technical memo based on all captured data turns time spent searching into time.
# wrap up
The “everything” notebook is a potentially transformative strategy for any data scientist looking to maximize productivity and ensure continuity of knowledge. Centralize LLM for deep synthesis and intelligent search, maximize capacity, and move you through the transition from fragmented file management to mastering a stable, intelligent knowledge base. This single repository becomes the single source of truth for your projects, domain expertise and company history.
Matthew Mayo For centuries.@mattmayo13) holds a Master’s degree in Computer Science and a Graduate Diploma in Data Mining. As Managing Editor of Kdnuggets & Statologyand contributing editor Expertise in machine learningMatthew aims to make complex data science concepts accessible. His professional interests include exploring natural language processing, language models, machine learning algorithms, and 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.