4D Research Loop for Better Product Management Research and Analysis
Learn how to overcome research limitations of popular AI assistants with minimal effort.
Last quarter, I was preparing for a critical stakeholder call when a competitor quietly released an AI-driven feature, literally days before my meeting. I turned to ChatGPT for a quick competitive analysis, only to realize its knowledge stopped in 2023. I needed a faster way to capture up-to-date insights without reinventing my research process every time.
Three recurring challenges:
- Stale Data: Even “recent” reports from 2024 or 2025 often referenced older stats or content.
- Generic Outputs: AI-generated research summaries felt one-size-fits-all, demanding heavy rewrites.
- Source Control : Deep research options addressed some of the above challenges, however it still did not offer granular control over my references.
This article lays out my 4D Research Framework, a proven process I use to ensure speed, accuracy, and context in every deliverable, based on my work.
The Goal
Create a simple workflow that brings in latest data and provides control on knowledge sources and analysis, for the context at hand.
4D Research Framework
Here’s the exact process I follow. It fuses AI assistant’s generative flair with NotebookLM’s source-grounded tooling for fast, reliable PM work.
| Phase | What I Do | Tools & Features |
| Discover | Generate Precise seed queries, questions to answer, etc | ChatGPT/Gemini Chat, Sometimes File uploads into Project folder in ChatGPT. |
| Dig In | Build a repo of up-to-date sources. About 200 sources for a given topic. | NotebookLM – Discover, Auto-Summaries, Mind mapping, Chat and Add Note. |
| Distill | Extract facts, stats, and summaries. Export (Copy) into a file for use outside Notebook LLM. | NotebookLM – Chat, Mind Maps, Add note, etc. |
| Deliver | Draft final PM assets. | Extracts in ChatGPT/Gemini, polished with prompts. Canvas. |
1. Discover (Seeding Queries)
How I Start:
I typically start with ChatGPT or Gemini for high-level brainstorming, exploring potential feature ideas, or generating a broad list of keywords and questions relevant to a new initiative. This provides a foundational set of search terms, phrases, topics and areas for deeper investigation.
Starter Prompt :
My goal is to build a deep and broad repository of sources (such as articles, academic papers, videos, infographics, case studies, tools, frameworks, expert opinions, podcasts, and industry reports) for a central knowledge base.
As a Product Management research assistant, generate a comprehensive and diverse list of 20-25 search terms, phrases, and questions to gather information on [INSERT TOPIC & KEY GOALS HERE]Review the list and iterate the list until you believe it covers all the angles you want to explore.
2. Dig In (Curating Sources)
Creating a Repo of Sources:
You can add webpage links, youtube links, copy-paste content, etc into Notebook LM, however, the one I use the most is the “Discover” feature. You can use the terms/phrases/questions from the previous step to search the internet and add 10 sources at a time. It is amazing that you can scrape a ton of information using custom terms and create a repo for yourself. I usually do a complete pass with all the terms and add about 200-250 sources. Then comes the Curation.
Image : Discover modal, Paste each item from previous step.
Note : Discover feature shows 10 results at a time, so if you have 20-25 search terms, you can get upto 250 sources.
Note : I use Pro version of Notebook LM and that allows me upto 300 sources. I target 200-250 sources at the beginning, leaving atleast 10-20% of source capacity open.
Note : Generate less than 20-25 terms in previous step if you want to capture less sources in your Notebook. Do not generate 20-25 and use only 10 out of it, you may leave critical topics or viewpoints out. Irrespective of the count, try to ensure they cover all viewpoints you need for your research and analysis.
Curate Sources:
Review NotebookLM’s auto-summaries to prune low-value or outdated docs. Just open the source and review the summaries generated. If needed, ask questions. I also ask in the chat if there are outdated sources or duplicate sources. The Mind Map feature really helps with curation and identify gaps. Rename sources if needed.
Image : Source auto-summary. Click on a source to see the summary.
After a few rounds of review, I usually end up removing 10-15% of the sources, leaving me some room to add a few more sources. Add more sources based on the gap analysis.
Note : Leave 10% of your source capacity for uploads or other content types, including content you might generate elsewhere and simply copy-paste.
Note : Check each source for both publication date and the recency of its data, trim anything that looks fresh but recycles old content.
3. Distill (Extracting Insights)
Inside NotebookLM, I start with broad questions then drill down into details, For example,
- Summarize Challenges in AI adoption in large enterprises?
- What AI techniques from the sources apply to our roadmap?
When you have something significant, Save the notes for later. It is also easy to clear everything and start over OR convert one of your notes into sources for extra emphasis.
Note : If you are unsure where to start? The starter prompts or Mindmap is a great place to start. I sometimes also use the Briefing Doc or FAQs.
Once I have all the notes, I export (copy-paste). I prefer to copy-paste as many of notes from Notebook LM into a single Notion / Google doc to ensure it’s all in one place. Then it’s time to upload it into my AI assistant of the day.
Compliance Note: Maintain one-way flow. I do not upload Private data or confidential information into Notebook LM (unless you are sure). I only export (Add note and then copy over) from Notebook LM to private space – ONE WAY and never in reverse.
4. Deliver (Drafting Assets)
I bring distilled insights into ChatGPT or Gemini (or Co-pilot for anything related to the client, since that’s what my client trusts). The loop doesn’t change—only the assistant does.
Now I am ready to use my favorite assistant with the latest information. Added bonus – I am very familiar with the reference material from handling it in the above steps. This allows me to make the best use of AI assistant’s capabilities and generate what I need more effectively. This process ensures no major blind spots and leverages each model’s strengths. I also switch the models within an AI assistant or across AI assistants, based on the type of output needed. For example, I start with a reasoning model in ChatGPT, after a few iterations, take it to Gemini and get it to review it, using a summary as the core.
Note : Iterate atleast a few times, with manual reviews in between. When i have my sources sorted, I usually achieve the best results in under 10 iterations.
Key Wins I’ve Seen
- Better Quality: I can build and refine a 200‑doc repo in a day, then generate stakeholder-ready drafts within the next few days. If its a minor topic the entire process might be done with lesser number of sources, in matter of hours. The process generates more detailed narrations with more broader research. So I don’t see it as a time saver, but a quality improvement.
- Trusted citations: Many times, stakeholders want to see the sources, and I have it all ready in a easy to find repo. I can also handpick my sources easily.
- Balanced perspectives: Cross-tool reviews and wide net, catch structural weakness with AI, keeping me out of model echo chambers.
Conclusion:
For PMs looking to overcome the inherent limitations of tools like ChatGPT/Gemini or any other Pre-trained AI Assistant, especially regarding data freshness and context specificity, combining them with a source-grounded tool like NotebookLM offers a powerful and practical solution.
This workflow has allowed me to focus on outcomes, leveraging the strengths of each AI tool more strategically without overcomplicating my processes. It’s all about making AI work effectively for the specific demands of product management.
What methods do you use to ground your AI-assisted PM work? Please share other practical workflows/process in the comments.