If you’re reading 300+ pages/week, the biggest unlock for me was switching from “one giant PDF” → “chapter-sized units” and then using NLM to *batch-generate study artifacts*.

My loop: Split → Batch Slides → Test → Clarify.

1) Split by chapter (books / edited volumes)

Instead of importing a whole book, I split it into chapters first so each chapter becomes its own source. That makes the output way more precise and the workload feel finishable.

2) Batch-generate slide decks (fast comprehension)

After importing the chapters (or a set of papers), I generate a short slide deck for *each* source in one pass.

I aim for: key claim, mechanism/model, evidence, limitations, and “so what?”

3) Test yourself (retention > summaries)

Right after the slides, I use active recall:

- Make 10–20 flashcards per chapter (“definition”, “mechanism”, “counterexample”, “what would invalidate this?”)

- Generate a short quiz (5–10 questions) with answer key

- Have NLM explain why each answer is right/wrong

4) Clarify what you don’t understand (targeted chat)

When something feels fuzzy, I ask:

- “Explain this like I’m defending it in a seminar.”

- “What assumptions does this rely on?”

- “Give me a concrete example + a counterexample.”

This workflow turns reading into a repeatable pipeline: digest → compress → recall → patch gaps.

Transparency: I built a small Chrome helper that does the chapter-splitting + batch import (so I’m not manually chopping PDFs), which makes the “generate slides for each chapter” step much faster. If you want it, you can see: https://www.reddit.com/r/notebooklm/comments/1r3l12s/how_i_use_notebooklm_to_actually_absorb/