Use Case #25: NotebookLM Preparation

Google's NotebookLM turns documents into conversational podcasts. It's genuinely impressive.

But the output quality depends heavily on input quality.

The Problem

Raw documents often have structure that confuses NotebookLM: tables that become word salad, code blocks that get read literally, acronyms that aren't expanded, context that's assumed but never stated.

The Solution

Before feeding documents to NotebookLM, Claude preprocesses them:

Tables → Prose - Convert data tables to written descriptions. "Revenue grew from $1M to $1.5M" instead of a table row.

Code → Concepts - Replace code blocks with plain English explanations of what the code does.

Acronym Expansion - First use of any acronym gets expanded. "Row Level Security (RLS)" not just "RLS."

Context Addition - Add brief introductions that frame the document. NotebookLM's hosts need to know what they're discussing.

Section Balance - Ensure roughly equal depth across sections. NotebookLM rushes through thin sections.

The Results

Podcasts from preprocessed documents are more coherent, cover topics more evenly, don't have awkward pauses where hosts struggle with data, and sound more like genuine conversations.

Example

Original: Dense technical specification with tables and code.

Preprocessed: Narrative description of the same content, optimized for audio.

NotebookLM output: 12-minute podcast that actually explains the concept clearly.

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I prepare client documentation this way before generating audio summaries.