Multi-turn conversations with AI require carefully managing conversation history and context. Naive approaches that include entire conversation histories in every request quickly become expensive and unwieldy. Effective state management balances context retention against API costs while ensuring AI responses remain coherent and contextually appropriate.
Context Window Management
LLMs have finite context windows that limit conversation length. Simple truncation of old messages preserves recent context but loses important historical information. More sophisticated approaches summarize older conversation segments, maintaining key information while reducing token usage. Selective inclusion of relevant prior messages based on semantic similarity provides dynamic context relevant to the current query.
- Implement sliding window approaches that keep recent messages plus summarized history
- Use semantic search to retrieve relevant prior conversation segments dynamically
- Store structured session data separately from conversation text for efficient access
- Compress repeated information like system prompts using provider-specific caching features
- Set conversation timeout periods that reset context after extended inactivity
Session Persistence Architecture
Persistent conversation state requires storage strategies that balance performance, cost, and data privacy. Redis provides fast session access for active conversations. Longer-term storage in databases enables conversation resume and analysis. Key-value stores offer cost-effective persistence for less frequently accessed sessions. The right architecture depends on your conversation volume and retention requirements.
Error Recovery and Resilience
Conversation systems must gracefully handle interruptions and errors. Saving state after each successful exchange enables recovery from failures. Conversation metadata tracking when state was last updated helps detect stale sessions. Clear error messages inform users when context is lost, rather than confusing them with responses that ignore previous exchanges.