Unverified50% confidenceFactExact time
The context window is short-term working memory that is cleared when the session ends; the model does not automatically solidify context information into parameters or external storage unless through mechanisms such as RAG, external databases, or explicitly writing to files.
1
Sources
50%
Confidence
Long-term
Relevance
10/23/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedCurrent LLMs' memory is primarily limited by the context window, making it impossible to truly retain information across multiple independent conversations.70% similarUnverifiedThe model API itself is stateless, so content that needs to be remembered must be placed back into the prompt with every request; the longer the context, the higher the token consumption and latency, and it may hit the context window limit.69% similarUnverifiedHard-coding the use of the file system for memory persistence in the prompt limits the model's ability to explore other solutions such as vector databases, structured databases, and knowledge graphs.69% similarVerifiedLarge models lack true long-term memory; every conversation starts from scratch, and they can only compensate by relying on external databases such as RAG (Retrieval-Augmented Generation).68% similarUnverifiedContext loss refers to a model forgetting earlier information after exceeding the context window limit when processing long conversations or long documents, leading to contradictions.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1089229API
curl https://kongchang.com/api/v1/knowledge/claims/1089229MCP
get_claim(id=1089229)