Type “summarize” into NotebookLM, and watch it quietly strip out half the details you actually needed. Type “explain” instead, and the same source material comes back richer, not thinner. A Redditor going by u/sumitsah_445 laid out the full method on r/PromptEngineering. It’s the clearest breakdown I’ve seen of how to actually research with this tool instead of just skimming it.
The core idea is simple: stop asking the AI to compress your sources, and start asking it to expand them. Compression drops detail by design. Expansion keeps it, then organizes it so you can actually use it. I tested this swap on a dense whitepaper myself, and the difference was striking!
Here’s the quick version. Index your sources first, then swap “summarize” for “explain.” Work through each topic one at a time instead of asking for everything at once. Do that, and NotebookLM stops acting like a compressor and starts acting like a research partner.
Old Way vs New Way
The old way: upload your PDFs, type “summarize this,” and get back a flattened, generic overview that drops the details you needed most. The new way: index first, explain instead of summarize, then go section by section so nothing gets compressed away. The original poster treats NotebookLM less like a search bar and more like a research assistant that needs clear instructions.
The 7-Step Method 🧭
- Index before you ask anything. Don’t jump straight to your real question. First ask NotebookLM this: “Index your sources into the main topics. Output only the topic titles.” If your PDF already has a table of contents, skip this step. For anything else, loose notes, scraped articles, non-book PDFs, this gives you a map before you start digging. Feed that list of topics back into your prompts, or better, drop it into NotebookLM’s custom instructions so every future chat already knows the structure.
- Say “explain,” never “summarize.” This is the single highest-leverage swap in the whole method. “Summarize” tells the model to compress, and compression means it quietly drops details you might have needed. “Explain” tells it to retain context and walk you through the material instead of shrinking it.
- Use NotebookLM’s built-in video overview for a first pass. For non-technical material, it works as a fast primer. If you’re learning something new, treat the generated video like a mini-course you watch before going deeper.
- Bring in DistilBook for a deeper video pass. This AI professional also runs a separate tool alongside NotebookLM. It’s built for more detailed video explanations and, in some cases, goes further than NotebookLM’s own video output. Worth testing side by side if video is how you learn best.
- Stick with NotebookLM for basic slide explanations. If all you need is a simple, slide-based walkthrough, don’t switch tools. This is one place NotebookLM already does the job well on its own.
- Deep-dive each topic from your index, one at a time. This is the step that actually separates a pro-level result from a generic one. This Redditor’s approach: take each title from your index and analyze it on its own. Ask NotebookLM to draw from every relevant source, explain the topic in depth, and connect information across documents. Make sure it includes evidence and context, and doesn’t leave anything important out. Doing this one topic at a time forces the model to dig through your documents properly instead of flattening everything into one pass.
- Tell it to take its time. Close every deep-dive request with a version of this line: “Take your time and do a deep dive into this topic. Analyze all relevant sources carefully and provide a comprehensive explanation. Don’t rush or omit important details.” The model doesn’t need time the way a person does, but the instruction still works. Think about the difference between telling a human researcher “give me a summary” versus “take your time and go through everything carefully.” You’d expect two completely different results, and the same logic applies here.
Try this on one PDF you’ve been meaning to actually understand, not just skim. Index it, explain it, then deep-dive one topic before moving to the next:
- ✅ Build your index first, always.
- 💡 Swap “summarize” for “explain” everywhere you research.
- 🔍 Deep-dive one topic before moving to the next.
The rest of the discussion on r/PromptEngineering has more back-and-forth worth reading. Other readers are already adapting this for comparing multiple PDFs on the same topic.
Frequently Asked Questions
Q: Does the ‘explain instead of summarize’ technique work with tools other than NotebookLM?
Yes. This principle generalizes across AI tools, ask for explanations rather than summaries to preserve source trails and underlying context. The real win is forcing the model to retain detail instead of compressing aggressively, which works with Claude, ChatGPT, and other LLMs.
Q: How do I compare and synthesize information across multiple unstructured PDFs on the same topic?
Create separate notebooks for each PDF and generate explanations or videos from each to understand their unique perspectives. Then manually cross-reference insights, or compile a structured index from all PDFs and feed it into a separate chat for comparative analysis.
Q: What’s the best way to maintain source attribution when using NotebookLM for research?
Use explanations rather than summaries, they keep source trails visible. Actively cite sources within NotebookLM as you work, and use Chrome extensions to assist with citation management. Track which sources answered which questions throughout your research process.
Q: When should I use NotebookLM versus Claude Code or other AI tools?
NotebookLM excels at exploring document-heavy research with built-in video generation and indexing. Claude Code is better for integrated workflows and flexibility. Many users combine both: NotebookLM for structured document understanding and Claude Code for broader tasks.
Q: Can I combine NotebookLM with other video generation tools like DistilBook?
Absolutely. Generate initial video overviews in NotebookLM, then feed those insights into DistilBook or other tools for deeper explanations. You can also store video transcripts back in NotebookLM for further analysis and refinement.
Stop asking NotebookLM to “summarize” your sources. Do this instead for pro-level research.
by u/sumitsah_445 in PromptEngineering