LLM-powered chatbots have improved dramatically, but maintaining a useful conversation over many turns still seems surprisingly difficult.
Once conversations become longer, we often start seeing problems such as:
- Important context being forgotten
- Irrelevant conversation history consuming tokens
- Hallucinations increasing as context grows
- User preferences being inconsistently remembered
- Retrieval returning technically relevant but contextually wrong information
- Latency and inference cost increasing
- Multilingual conversations losing meaning between turns
I’m curious how people here are handling conversation memory in production AI assistants.
Are you mainly using:
- Sliding-window conversation history?
- Summarization-based memory?
- Vector database retrieval?
- Structured user/session memory?
- Long-context models?
- A hybrid approach?
And perhaps more importantly: how do you evaluate whether an AI assistant actually remembers the right information rather than simply remembering more information?
Would love to hear what architectures or Hugging Face models/tools people are successfully using in production.