Unverified50% confidenceFactExact time
Context truncation is the most common source of confounding in long-input experiments: when the concatenated prompt exceeds the model's context window or the server-side max_tokens limit, the system usually silently discards content from the tail or the head.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedAn overly long system prompt occupies the context window and affects the model's understanding of user input.72% similarVerifiedWhen conversation history exceeds the context window limit, platforms use engineering methods such as truncation and summary compression to trim the context, a process that inevitably loses detail information.72% 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.72% 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.71% similarUnverifiedIn RAG scenarios, concatenating retrieved context documents into the Prompt causes a sharp increase in input tokens, and exceeding the context window limit results in API errors or truncated input.71% similar
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