Research is the first step in shaping the right product strategy. But for most Product Managers, desk research often means countless hours of being buried in market reports, interview transcripts, academic papers and internal documentation.
What if you could drastically reduce that time without compromising depth or rigour?
This is where NotebookLM enters the picture: an AI assistant by Google Labs that helps synthesise large volumes of content into actionable insights.
We’ll explore how PMs can integrate NotebookLM into their discovery workflow to accelerate early research, refine assumptions, and improve framing. And of course, all without losing control of source quality or context.
What is NotebookLM?
NotebookLM is an AI-powered research notebook. It allows you to upload documents (PDFs, Google Docs, etc.), organise them into notebooks, and interact with them using natural language prompts.
Think of it as an AI research analyst that:
- Understands dense material across multiple documents
- Surfaces cross-document insights and contradictions
- Generates summaries, drafts, and tables
- Always cites its sources, reducing hallucination risk.
This makes it especially useful for PMs dealing with fragmented knowledge, compliance-heavy domains, or complex technical markets.
How to use NotebookLM as a product manager
The value of NotebookLM depends on how you integrate it into your workflow. We recommend four simple steps:
Step 1: centralise your source material
Before asking good questions, you need good inputs. Start by uploading everything relevant to your problem space:
- Customer interviews and transcripts
- Market and trend reports
- Academic or regulatory papers
- Internal product docs, business memos, roadmaps
NotebookLM indexes your documents into a searchable, private knowledge base. The more complete your input, the stronger your insights.
This early-stage research directly supports the first steps in dualoop’s Product Initiative Document (PID), where you define context, strategic fit, and impacted metrics.
Step 2: ask targeted research questions
Now that your inputs are centralised, shift from passive reading to active investigation. Instead of scrolling through the documents, frame direct questions like:
- What trends appear in customer interviews across Q1–Q3?
- How do competitors package Feature X, and how do they describe it?
- What legal requirements must we follow for our upcoming initiative?
💡 Anchor your questions in your uploaded material. NotebookLM performs best with tight scoping and concrete prompts.
Step 3: identify themes, contradictions, and gaps
NotebookLM can connect dots across multiple files, and always shows the source behind each insight.
Use it to:
- Surface recurring pain points.
- Spot persona-specific contradictions.
- Uncover data or knowledge gaps.
- Generate argumentation with traceable references.
📌 This is ideal for feeding your assumption mapping process, one of the first steps in dualoop’s product discovery playbook.
Step 4: generate structured inputs
NotebookLM is not the place to write your final artefacts, but it can generate early drafts and structured material you will refine later:
- Problem statement drafts
- Summaries of key topics
- Internal FAQs
- Tables comparing perspectives or data points
- Stakeholder briefs
⚠️ It’s not ideal for final artefacts like Product Initiative Documents, but it’s excellent for generating inputs you’ll refine later.
Real-world example: researching hourly matching in energy
One of our consultants recently supported an energy-sector client on a go-to-market strategy for a product built around hourly electricity matching. It was in a complex space with dense regulation, fragmented research, and a mix of economic and technical factors. Normally, just the desk research phase would have taken weeks.
With NotebookLM, the approach looked very different:
- Collected academic papers and use-case reports.
- Started broadly: “What is the legal framework around hourly matching today?”
- Narrowed down: “What levels of hourly matching are economically feasible in Belgium today?” “What are the types of companies that would be most interested? Think in terms of electricity load profile.”
- Changed tone: prompted NotebookLM to act as a sales rep for concise pitch content towards potential users.
Result:
🕒 Research time cut by 80%
📚 High confidence in source quality
📈 More time spent framing hypotheses and aligning stakeholders
NotebookLM helped us think faster. It didn’t replace our judgement but gave us space to apply it better.
Prompting best practices
Use these patterns to get better results:
✅ Be specific:
→ “Based on uploaded customer feedback, what’s the biggest blocker to onboarding?”
✅ Ask for comparisons:
→ “Compare how Report A and Report B define product-market fit.”
✅ Request structure:
→ “Create a table with persona, pain point, and frequency.”
✅ Use the tool’s UI helpers:
→ One-click summaries and pre-filled suggestions often spark new research angles.
Attention points
- No web access, only works on uploaded documents.
- 50-document upload limit per notebook
- Requires structured input, avoid scanned PDFs or raw HTML
- Final outputs still need human validation.
- Best for input generation, not for artefact publishing.
How NotebookLM aligns with dualoop’s discovery approach
NotebookLM fits naturally into the first phases of our Product Execution Flow, where early framing and assumption mapping set the foundation for the entire initiative;
Here’s where it makes the biggest difference:
Problem framing: NotebookLM helps teams synthesise interviews, market studies, and internal documents into a clear picture of the problem space.
Assumption mapping: because the tool surfaces contradictions and knowledge gaps, it supports the process of mapping knowns and unknowns.
Trio kick-off: NotebookLM can generate structured briefs or hypothesis summaries that give the trio a running start.
Continuous discovery: NotebookLM helps maintain rhythm by producing quick summaries, insight tables, or persona updates that keep the team’s understanding fresh without slowing delivery.
Used this way, NotebookLM becomes part of the fabric of discovery work. It doesn’t replace the judgment of the product trio but it ensures their conversations start from evidence.
Final thoughts
NotebookLM won’t do your product thinking for you, but what it does brilliantly is reduce your time-to-insight by transforming static documents into dynamic knowledge.
In a world of information overload, that gives you a quiet edge.
It’s not here to replace your judgement, it’s here to give it room to breathe.
