Picture by Editor
# Introduction
Making a product necessities doc (PRD) is a standard course of in product administration and a commonplace activity in sectors like software program growth and the tech trade as a complete. A few of the sometimes discovered difficulties and onerous necessities in making a PRD embrace guaranteeing readability, stopping scope creep, and preserving stakeholder alignment.
Fortunately, AI instruments have risen to assist navigate these challenges extra successfully, with out fully delegating the strategic decision-making underlying the PRD creation course of — in different phrases, with the human nonetheless within the loop. One instance is Google’s NotebookLM, which synthesizes grounded uncooked knowledge or supplies to reply questions, thereby turbocharging the workflow for creating grounded, helpful PRDs.
This text will navigate you, primarily based on a beginner-friendly use case, by means of the method of utilizing NotebookLM’s options to show uncooked, typically chaotic data right into a grounded PRD in a matter of minutes. Spoiler: it will not be nearly chatting with an AI assistant.
# From Messy Notes to a Structured PRD Draft
Let’s think about the next state of affairs. You’re the newly employed product supervisor for a startup that wishes to develop a brand new cell app referred to as FloraFriend. The objective of the app is to assist folks cease unintentionally killing their houseplants.
The group, together with you, has collected a set of three “messy” paperwork that include descriptions for what the potential app ought to be like:
interview_transcript_matt.txt: a 30-minute interview with a person referred to as Matt, who’s the proprietor of over 50 crops. In these interview notes, Matt says present apps are “overly difficult” and make it troublesome to retain in thoughts points like “which fertilizer to make use of.”competitor_research_notes.txt: a tough checklist of bullet factors made after analyzing competitor apps like “PictureThis” and “Planta”, highlighting their paywalls and interface drawbacks.brainstorming_whiteboard.jpg: random however considerably “cool” concepts which have been talked about by the group throughout lunch breaks and different informal conversations, e.g. “spotify playlists for crops”, “watering reminders”, and so forth.
Think about full paperwork containing the entire content material described above. Manually turning these right into a clear PRD that properly brings all of it collectively could sound like a ache, proper? Enter NotebookLM!
Log in to NotebookLM along with your Google Account and click on “Create New Pocket book“. Give your new pocket book a reputation, one thing like “FloraFriend PRD.”
As soon as the brand new pocket book has been created, you will be welcomed to the principle NotebookLM interface, which seems like this:
NotebookLM Interface
A phrase of warning: this newly created pocket book just isn’t clever per se. It isn’t a daily giant language mannequin (LLM); it doesn’t know plant care or every other particular subjects. However we’re about to show it an “specific” Grasp’s diploma about it with our messy — but enlightening for the instrument — notes.
Suppose you may have the three above talked about recordsdata with some content material associated to the plant care app, or every other uncooked data recordsdata of your individual. You possibly can add them to the NotebookLM canvas through the use of the add button in the principle, central part.
As soon as uploaded, you may consider your pocket book as one thing much like a tiny, toy-sized retrieval-augmented era (RAG) system that may begin considering and behaving AI-like primarily based on the data it has entry to. In truth, with out asking it, by clicking on both one of many uploaded recordsdata on the left-hand facet, NotebookLM generates a concise, well-organized abstract of the contents in that file: that is referred to as a file’s Supply information.
Now comes the important thing half. We might merely ask within the chat field on the backside one thing like “Write a PRD”, and that is it. However we wish to do that correctly and supply clear, particular directions, and that entails some immediate engineering, particularly to pressure the newly born AI to prioritize what we would like our PRD to mirror: prioritizing the person issues over the random concepts generated by the group (with out completely neglecting them). Here’s a well-crafted immediate that works:
I’m the product supervisor for FloraFriend. Primarily based solely on these sources, draft a PRD.
Essential constraints:
1. Prioritize options that clear up the ache factors talked about in interview_transcript_matt.txt.
2. Exclude any ‘brainstorming’ concepts that do not immediately handle a person drawback.
3. Construction the output with these headers: Drawback Assertion, Core Options, Non-Purposeful Necessities (UI/UX), and Success Metrics.
Attempt adapting this immediate to your individual enterprise drawback or use case. As soon as despatched, likelihood is you’ll get a pleasant and clear PRD with key sections like Drawback Assertion, Core Options, Non-Purposeful (UI/UX) Necessities, Success Metrics, and so forth.
Apparently, the PRD accommodates one thing that appears like numerical citations you may hover on. Should you achieve this, you will note the supply (one of many supply recordsdata) pop up:
Earlier than accepting this primary PRD as it’s, do not forget that a primary draft isn’t good. Hold participating in dialog to regularly refine it, e.g. in the event you discover there’s a lacking monetizing part, ask: “Primarily based on the competitor_research_notes.txt, what monetization fashions are our rivals utilizing, and what ought to we keep away from?“. After that, manually examine the outputs, be certain they’re in line with the remainder of the primary PRD draft, and incorporate the principle monetization insights into it, both manually or by asking NotebookLM’s AI to take action — in the event you go for the latter, all the time examine what you get earlier than blindly approving it. Keep in mind: AI could make errors!
The icing on the cake is the Audio Overview part on the right-hand panel (Studio). By simply clicking on it, you’ll generate an audio overview of the data contained within the supply recordsdata. This is a wonderful method to take up data when studying is likely to be much less interesting, e.g. while you’re in your every day commute.
# Subsequent Steps
This text introduces NotebookLM’s capabilities to generate grounded PRD specs from uncooked, messy paperwork in a matter of minutes, taking very simple steps. From right here, a worthwhile subsequent step could possibly be resorting to Google’s Antigravity to show your PRD specification right into a practical software program prototype.
Iván Palomares Carrascosa is a frontrunner, author, speaker, and adviser in AI, machine studying, deep studying & LLMs. He trains and guides others in harnessing AI in the actual world.
Picture by Editor
# Introduction
Making a product necessities doc (PRD) is a standard course of in product administration and a commonplace activity in sectors like software program growth and the tech trade as a complete. A few of the sometimes discovered difficulties and onerous necessities in making a PRD embrace guaranteeing readability, stopping scope creep, and preserving stakeholder alignment.
Fortunately, AI instruments have risen to assist navigate these challenges extra successfully, with out fully delegating the strategic decision-making underlying the PRD creation course of — in different phrases, with the human nonetheless within the loop. One instance is Google’s NotebookLM, which synthesizes grounded uncooked knowledge or supplies to reply questions, thereby turbocharging the workflow for creating grounded, helpful PRDs.
This text will navigate you, primarily based on a beginner-friendly use case, by means of the method of utilizing NotebookLM’s options to show uncooked, typically chaotic data right into a grounded PRD in a matter of minutes. Spoiler: it will not be nearly chatting with an AI assistant.
# From Messy Notes to a Structured PRD Draft
Let’s think about the next state of affairs. You’re the newly employed product supervisor for a startup that wishes to develop a brand new cell app referred to as FloraFriend. The objective of the app is to assist folks cease unintentionally killing their houseplants.
The group, together with you, has collected a set of three “messy” paperwork that include descriptions for what the potential app ought to be like:
interview_transcript_matt.txt: a 30-minute interview with a person referred to as Matt, who’s the proprietor of over 50 crops. In these interview notes, Matt says present apps are “overly difficult” and make it troublesome to retain in thoughts points like “which fertilizer to make use of.”competitor_research_notes.txt: a tough checklist of bullet factors made after analyzing competitor apps like “PictureThis” and “Planta”, highlighting their paywalls and interface drawbacks.brainstorming_whiteboard.jpg: random however considerably “cool” concepts which have been talked about by the group throughout lunch breaks and different informal conversations, e.g. “spotify playlists for crops”, “watering reminders”, and so forth.
Think about full paperwork containing the entire content material described above. Manually turning these right into a clear PRD that properly brings all of it collectively could sound like a ache, proper? Enter NotebookLM!
Log in to NotebookLM along with your Google Account and click on “Create New Pocket book“. Give your new pocket book a reputation, one thing like “FloraFriend PRD.”
As soon as the brand new pocket book has been created, you will be welcomed to the principle NotebookLM interface, which seems like this:
NotebookLM Interface
A phrase of warning: this newly created pocket book just isn’t clever per se. It isn’t a daily giant language mannequin (LLM); it doesn’t know plant care or every other particular subjects. However we’re about to show it an “specific” Grasp’s diploma about it with our messy — but enlightening for the instrument — notes.
Suppose you may have the three above talked about recordsdata with some content material associated to the plant care app, or every other uncooked data recordsdata of your individual. You possibly can add them to the NotebookLM canvas through the use of the add button in the principle, central part.
As soon as uploaded, you may consider your pocket book as one thing much like a tiny, toy-sized retrieval-augmented era (RAG) system that may begin considering and behaving AI-like primarily based on the data it has entry to. In truth, with out asking it, by clicking on both one of many uploaded recordsdata on the left-hand facet, NotebookLM generates a concise, well-organized abstract of the contents in that file: that is referred to as a file’s Supply information.
Now comes the important thing half. We might merely ask within the chat field on the backside one thing like “Write a PRD”, and that is it. However we wish to do that correctly and supply clear, particular directions, and that entails some immediate engineering, particularly to pressure the newly born AI to prioritize what we would like our PRD to mirror: prioritizing the person issues over the random concepts generated by the group (with out completely neglecting them). Here’s a well-crafted immediate that works:
I’m the product supervisor for FloraFriend. Primarily based solely on these sources, draft a PRD.
Essential constraints:
1. Prioritize options that clear up the ache factors talked about in interview_transcript_matt.txt.
2. Exclude any ‘brainstorming’ concepts that do not immediately handle a person drawback.
3. Construction the output with these headers: Drawback Assertion, Core Options, Non-Purposeful Necessities (UI/UX), and Success Metrics.
Attempt adapting this immediate to your individual enterprise drawback or use case. As soon as despatched, likelihood is you’ll get a pleasant and clear PRD with key sections like Drawback Assertion, Core Options, Non-Purposeful (UI/UX) Necessities, Success Metrics, and so forth.
Apparently, the PRD accommodates one thing that appears like numerical citations you may hover on. Should you achieve this, you will note the supply (one of many supply recordsdata) pop up:
Earlier than accepting this primary PRD as it’s, do not forget that a primary draft isn’t good. Hold participating in dialog to regularly refine it, e.g. in the event you discover there’s a lacking monetizing part, ask: “Primarily based on the competitor_research_notes.txt, what monetization fashions are our rivals utilizing, and what ought to we keep away from?“. After that, manually examine the outputs, be certain they’re in line with the remainder of the primary PRD draft, and incorporate the principle monetization insights into it, both manually or by asking NotebookLM’s AI to take action — in the event you go for the latter, all the time examine what you get earlier than blindly approving it. Keep in mind: AI could make errors!
The icing on the cake is the Audio Overview part on the right-hand panel (Studio). By simply clicking on it, you’ll generate an audio overview of the data contained within the supply recordsdata. This is a wonderful method to take up data when studying is likely to be much less interesting, e.g. while you’re in your every day commute.
# Subsequent Steps
This text introduces NotebookLM’s capabilities to generate grounded PRD specs from uncooked, messy paperwork in a matter of minutes, taking very simple steps. From right here, a worthwhile subsequent step could possibly be resorting to Google’s Antigravity to show your PRD specification right into a practical software program prototype.
Iván Palomares Carrascosa is a frontrunner, author, speaker, and adviser in AI, machine studying, deep studying & LLMs. He trains and guides others in harnessing AI in the actual world.
