# Product Context
Last Updated: 2024-12-27

## Why we're building this
- To create an engaging Discord bot that learns from and interacts with server conversations
- To provide natural, contextually relevant responses using both Markov chains and LLM capabilities
- To maintain conversation history and generate responses that feel authentic to each server's culture

## Core user problems/solutions
Problems:
- Current Markov responses can be incoherent or lack context
- No semantic understanding of conversation context
- Limited ability to generate coherent long-form responses

Solutions:
- Integrate LLM to enhance response quality while maintaining server-specific voice
- Use existing message database for both Markov and LLM training
- Combine Markov's randomness with LLM's coherence

## Key workflows
1. Message Collection
   - Listen to channels
   - Store messages in SQLite
   - Track message context and metadata

2. Response Generation
   - Current: Markov chain generation
   - Proposed: Hybrid Markov-LLM generation
   - Context-aware responses

3. Training
   - Batch processing of channel history
   - JSON import support
   - Continuous learning from new messages

## Product direction and priorities
1. Short term
   - Implement LLM integration for response generation
   - Maintain existing Markov functionality as fallback
   - Add context window for more relevant responses

2. Medium term
   - Fine-tune LLM on server-specific data
   - Implement response quality metrics
   - Add conversation memory

3. Long term
   - Advanced context understanding
   - Personality adaptation per server
   - Multi-modal response capabilities