How do you prevent token bloat from long conversation histories?

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Accepted Answer

Preventing token bloat in long conversation histories is crucial for maintaining efficiency and cost-effectiveness in LLM interactions. One primary method is a sliding window approach, where only the most recent N turns or a fixed token limit of the conversation history is retained, effectively discarding older, less relevant context. Another powerful technique involves dynamic summarization of past conversation segments, condensing multiple turns into a concise summary that captures essential information while significantly reducing token count. Furthermore, key information extraction can be employed to identify and store only critical entities, facts, or user preferences, injecting these sparse but vital details into the prompt alongside the current turn. Advanced systems often combine these strategies, perhaps using a small sliding window supplemented by an evolving summary or extracted facts, to ensure contextual relevance without exceeding token limits.