# How NotebookLM Changed My Approach to Technical Reviews

*By [Gianfranco Mileo](https://gianfranco-mileo.com/) · Published March 8, 2026 · updated September 6, 2026 · 2 min read · Category: Tools & Resources · Tags: Productivity, Developer Tools, Program Management · Canonical: https://gianfranco-mileo.com/blog/how-notebooklm-changed-my-approach-to-technical-reviews*

> Deep technical reviews require understanding thousands of pages of context. Here's how I use NotebookLM to prepare for reviews in a fraction of the time.

## The Context Problem

A typical design review at Google involves a 30-50 page design doc, references to multiple related docs, a codebase, and months of historical discussion in comments and threads. As a TPM, I need to understand all of this context well enough to ask the right questions — not the surface-level questions that waste everyone's time, but the deep questions that uncover risks and assumptions.

That's where NotebookLM became indispensable.

## My Workflow

### Step 1: Upload Everything

Before a major review, I upload the design doc, related PRDs, previous meeting notes, and any relevant post-mortems into a NotebookLM notebook. This gives the model full context about the project.

### Step 2: Ask Structural Questions

I start with questions like:

- "What are the key assumptions in this design?"
- "What dependencies does this create on other teams?"
- "What's the rollback strategy if this fails?"
- "How does this compare to the approach we considered in [previous doc]?"

### Step 3: Identify Gaps

The most valuable output isn't what NotebookLM tells me — it's what it *can't* tell me. When the model says "I don't see this addressed in the documents," that's often the most important question to bring to the review.

### Step 4: Generate My Review Notes

I ask NotebookLM to create a structured set of review comments organized by theme: architecture, scalability, testing, dependencies, and timeline. I then edit these into my own voice and add my specific concerns.

Related: see [The TPM's Guide to Evaluating AI Tools Without the Hype](https://gianfranco-mileo.com/blog/the-tpms-guide-to-evaluating-ai-tools-without-the-hype) and [Building Android at Scale: What Managing Camera & Media Taught Me About AI Integration](https://gianfranco-mileo.com/blog/building-android-at-scale-what-managing-camera-and-media-taught-me-about-ai-integration).

## The Result

My review prep time dropped from 3-4 hours to about 45 minutes. But more importantly, the *quality* of my reviews improved because I'm no longer spending cognitive energy on comprehension and can focus entirely on analysis and judgment.

The engineers I work with have noticed. One tech lead told me: "Your review comments are the most useful because you clearly read everything." I didn't correct them.

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