Unverified50% confidenceFactExact time
An overly long system prompt occupies the context window and affects the model's understanding of user input.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedFactors influencing users' perception of model degradation include context window filling, system prompt changes, sampling temperature adjustments, and model drift.79% similarUnverifiedContext window pressure causes early content to be truncated or compressed once conversation history, memory, and tool outputs fill the window, making the input the model actually receives different from the previous run and thus producing different reasoning paths.77% similarUnverifiedIn large-context-window scenarios, the risk of prompt injection is amplified, because the model must read a large amount of external material at once, and malicious instructions may be scattered in different positions within the long text and be harder to find via keyword filtering.73% similarVerifiedThe larger the context window, the more information the model can remember in a single call, but it also brings rising inference costs and the problem of intermediate information being ignored.72% similarUnverifiedContinuously correcting the model stuffs more contradictory and redundant information into the context, making the model more confused, and corrections often backfire.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1071436API
curl https://kongchang.com/api/v1/knowledge/claims/1071436MCP
get_claim(id=1071436)