Frontier AI, Frontier Engine Math, & New Models
Long-Term Memory for LLMs with Vector Stores
Long-term memory for LLMs stores past interactions, facts, and documents in a vector store and retrieves them when relevant. Done well, it makes assistants coherent across sessions; done badly, it injects stale or wrong context and degrades answers. Memory is a data problem, not a prompt trick.
Key points
- Store atomic, timestamped facts with source and confidence, not raw chat logs.
- Retrieve by relevance plus recency; pure similarity returns stale context.
- Summarize and consolidate memories periodically to control growth and noise.
- Scope memory per user and per tenant; never mix across accounts.
- Let users view, edit, and delete their memories for trust and compliance.
- Validate retrieved memories before injecting them into the prompt.
- Measure memory precision and recall on labeled queries.
- Expire or archive low-value memories to keep retrieval sharp.
Frequently asked questions
Is vector memory the same as RAG?
It is RAG over your own interaction history and facts; the retrieval challenges are the same.
How do I prevent stale memories from hurting answers?
Timestamp, score recency, and consolidate or expire old entries.
Can memory leak across users?
Yes if scoping is wrong; enforce tenant and user filters at retrieval time.
How much memory should I store?
Store what improves answers and passes evals; more is often worse due to noise.
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