NotebookLM

NotebookLM takes a focused approach to AI:

Document-grounded responses: Answers come from your uploaded sources, not from the model's general knowledge. Reduces hallucination risk.

Citation support: Responses point to specific passages in your documents. Users can verify claims.

Synthesis across sources: Combines information from multiple documents to answer questions that span your knowledge base.

Audio overview generation: Creates podcast-style summaries of your content for a different way to engage with materials.

NotebookLM suits scenarios involving document-based knowledge:

Research support: Academics, analysts, or strategists working with large document collections. Query papers, reports, and notes through conversation.

Policy and procedure: Making internal manuals, guidelines, and policies accessible through natural questions rather than keyword search.

Training and onboarding: Helping new staff learn from existing documentation without requiring someone to answer every question.

Client knowledge: Professionals managing information about clients, projects, or cases. Quick access to specifics buried in files.

NotebookLM works well within its scope but has boundaries:

What works well: Answering questions from uploaded documents. Responses are grounded in what you provide.

Finding specific details. Useful when information is buried across long documents.

Summarising and synthesising. Pulling themes across multiple sources into a coherent view.

Maintaining conversational context. Following multi-step questions without losing the thread.

Current limitations: Enterprise controls are still evolving. Governance and admin features may be less mature than enterprise-first platforms.

Integration options are limited. NotebookLM is not designed as a deeply embedded application platform.

Format and size constraints. What you can upload and how it behaves depends on document characteristics.

Not designed for live external data. It’s optimised for working from provided sources, not real-time web or system-of-record queries.

NotebookLM occupies a specific niche:

Simpler than full RAG: Easier to set up than building a retrieval-augmented generation system from components.

More focused than general AI: Better for document-grounded answers than general-purpose chatbots.

Complementary to other tools: Often works alongside broader knowledge management and AI deployments.

For organisations needing sophisticated document AI with custom integrations, purpose-built RAG systems may be more appropriate. For focused use cases, NotebookLM offers fast time to value.

The key design decision is governance: which documents are authoritative, who can access them, and how you prevent stale or contradictory sources from undermining trust. A small amount of curation often matters more than model sophistication.

We assist organisations with NotebookLM in several ways:

Evaluation: Determining whether NotebookLM suits your requirements versus alternatives.

Setup and configuration: Getting started with appropriate document organisation and settings.

Use case development: Identifying where NotebookLM adds value in your workflows.

Broader strategy: Placing NotebookLM within your overall approach to knowledge management and AI.

Use these prompts to decide whether a document-grounded approach is right for your organisation.

“Which knowledge sources should we include first, and what is the ‘system of record’ for each?”

“What governance controls do we need: access, redaction, audit logs, and safe sharing?”

“What success metrics will prove this helps: time saved, answer accuracy, and user adoption?”