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Context Rot and Long-Context Failure Modes
Context rot is the practical degradation of model quality as the input window fills up, even when the official window is a million tokens. Information in the middle gets ignored, attention concentrates on the first and last tokens, and reasoning that needs several scattered facts at once falls apart. Applied AI interviews probe it because candidates routinely assume a large window is a substitute for retrieval, and it is not.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsHow do you evaluate long-context models (needle-in-a-haystack and beyond)?→RAG & Agent System DesignContext windows are now huge. When do you just stuff everything in context instead of building RAG?→LLM & GenAI FundamentalsWhat is an attention sink, and how does StreamingLLM use it for endless generation?→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→ML Infrastructure & GPUsHow do you quantize or compress the KV cache, and why does it matter for long-context serving?→System Design for AI in ProductionDesign an AI meeting summarizer that handles thousands of meetings a day.→
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