<aside> đź›  Check the working prototype here (Note: The responses are tailored for basic system design-based questions to showcase the basic working of the Co-Pilot) - https://creator.voiceflow.com/prototype/65d876c7714cff74bafffde2

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đź“ť Description: What is it?

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I aim to develop an AI-powered LinkedIn Copilot that leverages Natural Language Processing (NLP) and Machine Learning (ML) to provide specific, personalized responses based on user prompts, filters, and connections. It will curate and present relevant material such as posts, articles, videos, etc., from LinkedIn based on the user’s query.

That is the output will be of two columns: a. Recommendation of the post that is relevant to the prompt by fetching the URL of that post and giving the top 3/5 posts in that domain.

b. And the second will be the regular GPT response which can be done by training GPT models of OpenAI on linkedIn’s data.

For eg: if i want to see questions on system design for interview of a product management opening for a fresher and the sector is edtech. so the copilot should get me relevant material to read from like posts, articles, videos etc that are posted on linkedin and a tailored reponse from the GPT.

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🔧 Problem: What problem is this solving?

<aside> <img src="/icons/pencil_gray.svg" alt="/icons/pencil_gray.svg" width="40px" /> The vast amount of information on LinkedIn can be overwhelming for users, making it difficult to find specific, relevant content. This problem is exacerbated by the scattered nature of the content and not having relevant connections or followers for the subject.

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🔎 Why: How do we know this is a real problem and worth solving?

  1. User Feedback: Users often express difficulty in navigating through the vast amount of content on LinkedIn to find specific, relevant information. This feedback indicates a real problem that needs addressing.
  2. Efficiency: Currently, users have to manually search and filter through numerous posts, articles, and videos to find the information they need. This process is time-consuming and inefficient. An AI-powered tool like the LinkedIn Copilot could significantly enhance efficiency.
  3. Personalization: Personalized content delivery is a key trend in today’s digital world. Users increasingly expect content to be tailored to their specific needs and interests. The LinkedIn Copilot aligns with this trend by providing personalized responses based on user prompts, filters, and connections. This not only solves a real problem but also adds value to the user experience.

🏆 Success: How do we know if we’ve solved this problem?

  1. Content Relevance Score/Month: This could be measured as the average relevance score of the content provided by the LinkedIn Copilot per month. The relevance score could be calculated based on user feedback or click-through rates on the provided content. A higher relevance score would suggest that the tool is effectively providing users with the information they need.
  2. User Engagement/Day: This could be measured as the average number of queries made by each user to the LinkedIn Copilot per day. A higher number would indicate that users are finding the tool useful and are engaging with it regularly.
  3. Session Duration/Hour: This could be measured as the average time spent by each user on the LinkedIn Copilot per hour. Longer session durations would suggest that users are spending more time interacting with the tool, indicating higher engagement and potentially higher satisfaction.

🌍 Audience: Who are we building for?

  1. Active LinkedIn Users: These are users who are highly active on LinkedIn, regularly posting, commenting, and engaging with content. They are seeking to leverage the LinkedIn Copilot to find more relevant and specific content based on their interests and needs.

  2. Passive LinkedIn Users: These users are on LinkedIn but do not engage much with the content. They might be overwhelmed by the amount of information available and would benefit from a tool like LinkedIn Copilot that can curate specific content for them.

  3. LinkedIn Learners: These are users who actively use LinkedIn Learning for their professional development. They are seeking to find more relevant learning resources and would benefit from LinkedIn Copilot’s ability to curate specific learning content based on their queries.

    We are prioritizing Active LinkedIn Users as this segment is already engaged on the platform and would likely be more open to using a tool like LinkedIn Copilot.

    Persona:

    Name: Professional Priya

    Age: 28

    Interests: Product Management, Networking, Professional Development

    Pain points:

🔬 What: Roughly, what does this look like in the product?

(Click where the finger is pointing in the wifeframe)