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Product Requirements Document: LLM-Powered Content Scoring and Summarization

Overview

This document outlines the requirements for an LLM-powered system that analyzes content, generates relevance scores based on user profiles, and provides personalized content filtering. This system is a core component of the InsightHub platform, aiming to deliver a highly personalized and relevant content experience to users. It solves the problem of information overload by surfacing content that aligns with a user's specific interests and expertise.

Core Features

  • LLM-based Content Analysis and Scoring:

    • What it does: Analyzes content from sources like YouTube and Reddit against a user's profile to generate a relevance score (0-100), a list of relevant categories, an explanation for the score, and a concise summary.
    • Why it's important: This is the core of the personalization engine, ensuring users see content that matters to them.
    • How it works: A ContentAnalyzer class will use LangChain and an OpenAI model to process content and user data, returning a structured ContentRelevance object.

  • Personalized Content Filtering:

    • What it does: Filters the incoming content feed, showing only items that meet a minimum relevance threshold.
    • Why it's important: Prevents users from being overwhelmed by irrelevant content, improving engagement.
    • How it works: A ContentFilter class will use the ContentAnalyzer to score a list of content items and return only those that pass the relevance threshold.

  • User Feedback Loop:

    • What it does: Allows users to provide feedback (e.g., like/dislike) on content, which then updates their user profile.
    • Why it's important: Continuously improves the accuracy of the personalization algorithm over time.
    • How it works: A FeedbackProcessor will adjust the weights of a user's interests in their profile based on their interactions.

User Experience

  • User Personas: The primary user is a professional or enthusiast who wants to stay up-to-date on specific topics without wading through irrelevant noise.
  • Key User Flows:
    1. User onboards and defines their interests.
    2. User browses a personalized feed of content.
    3. User consumes content and provides feedback (likes/dislikes).
    4. The system learns from feedback and further refines the feed.
  • UI/UX Considerations: The UI should clearly display the relevance score and summary for each piece of content. Feedback mechanisms should be simple and intuitive.

Technical Architecture

  • System Components:
    • ContentAnalyzer: Python class using LangChain and OpenAI.
    • ContentFilter: Python class to filter content lists.
    • FeedbackProcessor: Python class to handle user feedback.
    • ContentScorer: Orchestrator node to integrate the analyzer.
  • Data Models:
    • UserProfile: Pydantic model for user preferences.
    • ContentRelevance: Pydantic model for the output of the analysis.
  • APIs and Integrations:
    • OpenAI API for LLM access.
    • Supabase for storing user profiles and content metadata.
    • Integration with existing YouTube and Reddit data pipelines.
  • Infrastructure Requirements: Standard Python environment with necessary libraries. No major infrastructure changes are required.

Development Roadmap

  • MVP Requirements:

    1. Implement the UserProfile and ContentRelevance Pydantic models.
    2. Build the core ContentAnalyzer class.
    3. Develop the ContentFilter and integrate it into the existing content pipelines.
    4. Update the ContentScorer orchestrator node.
    5. Modify the database schema to store relevance scores.

  • Future Enhancements:

    1. Implement the FeedbackProcessor to enable the user feedback loop.
    2. Develop a more sophisticated user interest model.
    3. Add support for more content sources.
    4. Explore different LLMs for analysis.

Logical Dependency Chain

  1. Foundation: The UserProfile and ContentRelevance models must be defined first, as they are the data contracts for the system.
  2. Core Logic: The ContentAnalyzer is the next critical piece, as it contains the core intelligence.
  3. Integration: The ContentFilter can then be built, followed by its integration into the YouTube and Reddit processors and the orchestrator.
  4. Persistence: The database schema must be updated to store the output of the analysis.
  5. Feedback Loop: The FeedbackProcessor can be built last, as it is an enhancement to the core filtering functionality.

Risks and Mitigations

  • Technical Challenges:
    • Risk: The LLM may not provide consistently accurate relevance scores.
    • Mitigation: Extensive testing with a diverse set of content and user profiles. Fine-tuning prompts and potentially the model itself.
  • MVP Scope:
    • Risk: The MVP scope could become too large.
    • Mitigation: Strictly adhere to the defined MVP requirements and defer enhancements to a later phase.
  • Resource Constraints:
    • Risk: OpenAI API costs could be higher than expected.
    • Mitigation: Implement caching for content analysis results. Monitor API usage closely and optimize where possible.

Appendix

  • Research Findings: Initial research indicates that using LangChain with Pydantic output parsers is an effective way to get structured, reliable data from LLMs. The key is well-crafted prompts that clearly define the desired output format.
  • Technical Specifications: The detailed implementation plan can be found in the Taskmaster task description for ID #34.