Strategies and best practices¶

NotebookLM, like any tool, has specific limitations. Considering them as design characteristics rather than impediments allows you to manage them strategically. In this chapter, we describe NotebookLM's main constraints and strategies for navigating them effectively, transforming potential limitations into opportunities for more effective tool usage.

Understanding the limits¶

Before exploring specific strategies, it's important to understand NotebookLM's main structural limits. Beyond those already seen, there are also what we might call qualitative ones:

• Exclusive dependence on textual content of loaded sources
• Inability to process purely musical or non-verbal audio/video content
• Lack of automatic updating for modified Google Drive documents
• Limitations in integration with external ecosystems
• Constraints in visual interface customization

Recognizing these limits doesn't diminish the tool's value but allows developing intentional strategies to optimize its use within available parameters.

Experimentation and iteration: finding your ideal configuration¶

NotebookLM is becoming increasingly structured, perhaps even complex. Learning to use it isn't a process completed in one go, but a continuous exploration that benefits from experimentation and iteration. We recommend:

• Trying different stylistic configurations and comparing results
• Gathering feedback from other users to identify the most effective styles for different audiences
• Progressively refining customization instructions based on practical experience
• Adapting configurations as your understanding of topics and needs evolve

This iterative exploration not only improves the NotebookLM experience but also deepens understanding of how you communicate and work with information, offering insights into your own cognitive and communication style.

Source management strategies¶

Source management represents one of the most important areas for overcoming NotebookLM's limitations. Here are some techniques:

• Strategic segmentation: instead of loading very long documents as single sources, consider dividing them into smaller thematic sections. This improves response precision by making it easier for the system to identify pertinent information.
• Preliminary distillation: before loading particularly verbose or redundant sources, consider a "distillation" phase where you extract and condense the most relevant information. This preliminary process can transform extensive and dispersive documentation into more concentrated and usable sources.
• Multi-notebook organization: rather than trying to fit all sources related to a topic into a single notebook, develop an architecture of interconnected notebooks, each focused on specific aspects of the general theme. This approach allows overcoming the per-notebook source limit while maintaining an organic view of the topic.
• Organization within a notebook: in the sources column, the list is alphabetically ordered, and long titles are partially visible due to column width limits. When a notebook contains many sources, it can be useful to rename them by prepending a code that allows grouping them based on desired operations. For example, if texts from multiple authors are collected and you want to analyze by individual author, inserting the name at the beginning of the title allows quickly selecting a specific author's works.
• Cyclical updating: for sources that are frequently updated (like Google Drive documents), implement a regular update cycle where you remove the previous version and load the updated one. This practice, though manual, ensures the notebook always reflects the most recent information.

Maximizing daily interactions¶

Limits on daily interactions (chat questions and audio generations) can represent a significant constraint, especially for intensive users. Here are some strategies to maximize the value of each interaction:

• Query planning: before starting a session, plan the most important questions in advance. This preparation reduces "exploratory" questions and ensures each interaction generates significant value.
• Composite queries: structure questions that incorporate multiple related requests, such as "Summarize concept X, compare it with Y, and identify the main applications in context Z". This approach allows extracting more information from a single interaction.
• Using prompts for structured responses: explicitly request responses that organize information into dense and informative structures, such as comparative tables or hierarchical lists. This maximizes the amount of information obtained per query.
• Systematic saving: adopt the habit of saving every potentially useful response in the Studio area. This allows progressively building a permanent knowledge base that reduces the need to repeat similar questions in future sessions.

Formulating effective questions¶

Response quality depends on question clarity. Some strategies:

• Specific questions: instead of "Tell me about this topic", prefer "What are the three main factors influencing this phenomenon according to the sources?". Specific questions guide toward more focused responses.
• Analysis requests: NotebookLM is effective at analyzing relationships and trends. Questions like "How has this theory evolved over time?" or "What are the main differences between these two approaches?" produce useful results.
• Context clarification: if you're interested in a particular perspective, make it explicit: "Considering only the most recent sources, how is this process described?" or "According to author X, what would be the best solution?".
• Structured synthesis requests: you can guide the response format with requests like "Create a comparative table of the different methods mentioned" or "Provide a numbered list of necessary steps".
• Follow-up questions: the most productive conversations arise from a series of interconnected questions. Progressively deepen topics based on previous responses.
• Dynamic customization: NotebookLM allows dynamically defining interaction modes through specific question formulation. This form of contextual customization allows varying the AI's approach even within the same session, adapting it to different momentary needs. For example, you can include specific instructions in questions like:
  • "Provide a simplified explanation of this concept, suitable for a beginner"
  • "Critically analyze this theory highlighting strengths and weaknesses"
  • "Create a comparative table highlighting differences between these approaches"
  • "Synthesize this information in no more than three key points"

This flexibility allows using general settings for most interactions and specifying particular requests when necessary, avoiding frequent changes to base configurations.

Creating thematic environments: notebooks with distinct personalities¶

An advanced approach to customization consists of creating different notebooks, each with distinct configurations and conversational styles, dedicated to specific purposes. This allows quickly switching between different "thematic environments" optimized for different types of activities. For example:

• Thematic organization: create separate notebooks for distinct topics. This approach keeps responses focused and reduces information dispersion.
• Project organization: gather all sources related to a project in a single notebook, regardless of their thematic diversity. This allows exploring cross-cutting connections.
• Chronological organization: for topics that evolve over time, organize sources in chronological order, progressively adding new materials.
• Dynamic selection: maintain all sources in a single notebook, selecting or deselecting specific subsets before asking questions. The chat area is only active if at least one source is selected.

This multi-notebook strategy allows optimizing not only stylistic configurations but also source and note organization, creating distinct information environments for different usage contexts.

Integration strategies with other tools¶

No tool is isolated, and NotebookLM is no exception. Strategically integrating it with other tools in your digital ecosystem can significantly expand its capabilities:

• Integration with other AIs: it has long been possible to connect NotebookLM with Claude through dedicated MCP servers, combining the best of both. Additionally, Google recently started rolling out the ability to import a notebook as a data source - already-enabled accounts can do this in Gemini chat by clicking the + icon that allows adding reference sources. In the source list they'll find NotebookLM; selecting this option opens a popup with the list of notebooks defined in the NotebookLM account managed with the same Google account.
• Source selection with search tools: use specialized search engines and databases to identify relevant sources before importing them into NotebookLM. This "pre-selection" process ensures each loaded source offers significant informational value.
• Workflows with writing tools: develop workflows connecting NotebookLM to writing and publishing tools. For example, you could use NotebookLM for initial research and analysis, export results to a text editor for creative elaboration, and finally publish content through specialized platforms.
• Document preparation pipelines: create document preparation pipelines using OCR, transcription, and formatting tools to transform content not directly supported (like book scans or complex audio recordings) into formats importable into NotebookLM.
• Complementary annotation systems: alongside NotebookLM, use specialized annotation systems that allow enriching sources with metadata, links, and classifications, thus creating a richer and more contextualized information ecosystem.

Community and support resources¶

An additional dimension for overcoming tool limitations consists of accessing and contributing to user communities and support resources:

• Forums and user groups: participate in forums and groups dedicated to NotebookLM to share experiences, strategies, and creative solutions. These communities are often sources of innovative techniques not found in official documentation. An example is the Reddit community.
• Feature requests: actively participate in tool improvement by reporting bugs and suggesting new features. NotebookLM is continuously evolving, and user feedback plays an important role in defining its future capabilities. Very useful for this is the official Discord server managed by the development team.
• Tutorials and use cases: explore detailed tutorials and professional use cases illustrating how other users have creatively overcome tool limitations in contexts similar to your own. YouTube is the reference.

Accepting limits as creative opportunities¶

Finally, it's important to recognize that some limits aren't simply obstacles to overcome but should be seen as opportunities.

• The value of selection: the source number limit invites us to be more selective and intentional in choosing materials, a process that in itself can significantly improve the quality of thinking and analysis.
• The power of synthesis: size limits push us toward the art of synthesis and distillation, increasingly valuable skills in an era of information abundance.
• Intentionality in interactions: limits on daily interactions encourage us to be more deliberate and reflective in our questions, counteracting the tendency toward impulsive and superficial interaction with digital systems.

This approach transforms NotebookLM's limits into opportunities for interacting with information more consciously and strategically, fostering a more solid understanding of content.