Unverified50% confidenceFactExact time
Typical symptoms of poor context management are that the model becomes more forgetful the longer the conversation goes, repeatedly calls the same tool or ignores system instructions, and such problems are almost never exposed at the demo stage.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
AI Agent Engineer Practical Guide: From Getting the Model Running to System Deployment
bilibiliLLM应用开发10/19/2026
Related Entities
Related Claims
UnverifiedCurrent model instruction-following failures commonly manifest as ignoring system prompt constraints, forgetting early instructions in long conversations, randomly violating formatted output requirements, and deviating from predefined processes in multi-step tasks.77% similarVerifiedPrompt engineering has inherent limitations: the effectiveness of instructions decays over conversation turns (instruction forgetting), and different model versions respond significantly differently to the same instruction.75% similarUnverifiedWhen the context window approaches its limit, the model does not directly throw an error; instead, it shows symptoms of quality degradation such as forgetting earlier instructions, duplicated work, broken logic, and increased hallucination.74% similarUnverifiedFine-tuning carries the risk of catastrophic forgetting, where fine-tuning for a specific behavior may degrade the model's performance on other tasks.73% similarUnverifiedStreaming models have more failure modes than batch models and often hallucinate additional speakers out of thin air, which is a direct consequence of insufficient context.73% similar
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