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How to Build an AI 'Second Brain' for Product Management and Customer Feedback Analysis

Offload the mental burden of context switching by creating a dedicated AI assistant in Claude or ChatGPT. This 'second brain' holds all your project context and can even scrape and analyze thousands of customer conversations from sites like Reddit.

From How I AI

How I AI: 3 Game-Changing Workflows for Product Managers

with Claire Vo

How to Build an AI 'Second Brain' for Product Management and Customer Feedback Analysis

Tools Used

Claude

Anthropic AI assistant

ChatGPT

OpenAI conversational AI

Step-by-Step Guide

1

Lay the Foundation for Your AI Brain

Begin by feeding your AI project (in Claude or a custom GPT) a foundational set of documents. This gives the AI a solid understanding of your project's mission, product, and goals. Upload company kickoff decks, PRDs, and even PDFs of your public website pages.

Pro Tip: Adopt an 'everything is text' mindset. Print web pages or convert slides to PDF to broaden the scope of what your AI brain can learn from.
2

Generate a Web Scraper with Claude

Use a large language model like Claude to write a Python script that automatically gathers customer conversations. You don't need a deep technical background; just describe your goal and ask for step-by-step instructions.

Prompt:
I wanna find everything that's written about [monday.com], whether it be on Reddit, Twitter, LinkedIn, anything that's online. I wanted like a sort of an automated freeway to do this.
3

Analyze Scraped Data with AI

Once the script generates a .csv file of conversations, upload it back into your AI project. Ask the AI to act as a data analyst and summarize the findings into a structured format.

Prompt:
Here, I'm giving you this, this file. This is one of the files that was pulled out with 30,000 rows, of conversations. And I said, I want you to summarize it. In a table, where you put like the frequency, so how often it comes up, at what percentage I need weights because I need numbers to go back to my team and for myself to know what to prioritize, see what the biggest hottest topics are that people are, are discussing, and some of the key discussion points.
4

Verify AI Analysis and Integrate Insights

Always double-check the AI's work. Ask it to provide specific quotes or references from the original data for its claims. Once verified, upload the summarized insights back into your AI 'brain' to deepen its knowledge base.

Pro Tip: This verification step is crucial for ensuring the AI isn't hallucinating and that your product decisions are based on real data.
5

Configure the AI Brain's Persona

Give your AI specific instructions on how to behave. Tell it to act as a professional product manager, to challenge your ideas, and to provide candid, honest feedback rather than being overly agreeable.

Prompt:
I also gave it like a lot of like instructions on, you know, you know how to give feedback to me, you know how to give candid feedback and you're not gonna be like, too nice to me. You know how to challenge. It's 'cause I hate it when the AI will be like super supportive of every idea that I come up with, like push back on things.

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