TL;DR

The “everything notebook” approach in NotebookLM creates a centralized, searchable repository of domain knowledge by consolidating scattered documentation into a single queryable knowledge base. NotebookLM supports up to 50 sources per notebook containing 25 million words total, with individual sources accommodating 500,000 words. The strategy moves beyond static file storage by leveraging the platform’s LLM to uncover connections between disparate information sources, reducing cognitive load for complex projects.

Opening

Data science projects depend on foundational knowledge—organizational protocols, domain standards, mathematical libraries—scattered across multiple locations. NotebookLM’s “everything notebook” concept offers a centralized knowledge management approach that transforms static repositories into queryable knowledge bases.

Context: From Repository to Knowledge Graph

The everything notebook strategy involves three key steps. First, designate one notebook as the central repository loaded with core company documents, research papers, internal documentation, and code library guides. This repository functions as a living document that grows with each completed project—final reports, key code snippets, and post-mortem analyses are immediately ingested.

Second, maximize NotebookLM’s capacity. The platform handles 50 sources per notebook with 25 million words total capacity. A practical approach consolidates smaller documents—meeting notes, internal wikis—into 50 master Google Docs. Since each source accommodates 500,000 words, this consolidation dramatically expands available capacity.

Organizing consolidated documents by domain or project phase aids focused searching and improves the LLM’s contextualisation capabilities. For instance, “Project Management & Compliance Docs” could contain regulatory guides and risk assessments, whilst “Technical Specifications & Code References” houses library documentation and deployment guides.

Third, leverage the platform’s synthesis capability. By ingesting diverse sources—technical specifications, project ideas, meeting notes—NotebookLM’s LLM can uncover connections between seemingly unrelated information. This transforms the repository from simple file storage into a knowledge graph where the system understands relationships between concepts.

Looking Forward

The everything notebook concept represents a shift from document retrieval to knowledge synthesis. Rather than remembering where information lives or manually connecting disparate pieces, data science teams can query their entire professional memory through a single interface.

The approach’s effectiveness depends on consistent maintenance—treating the notebook as version control for knowledge rather than a one-time setup. As NotebookLM evolves, the platform’s ability to surface relevant connections and synthesise information from massive document collections will determine whether this strategy scales beyond individual users to team-wide knowledge management systems.

Source: KDnuggets