Context Windows: Limits, Lost-in-the-Middle, and Long-Context Myths (ANSWERED)
Scenario question on context windows — token budgets, lost-in-the-middle bias, and why RAG still matters despite 1M-token claims.
TL;DR — Quick Answer
Context window is the max tokens (prompt + completion) a model can process. Larger windows do not guarantee the model uses all tokens equally — lost-in-the-middle research shows models often underweight information in the middle of long prompts. Long-context marketing exceeds practical reliability for needle-in-haystack tasks at scale. Production systems still use RAG, summarization, and hierarchical retrieval because of cost, latency, attention degradation, and cache limits.
The Interview Question
Explain context window limits in LLMs. What is lost-in-the-middle, and what myths exist about long-context models in production?
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Context WindowLost-in-the-MiddleRAGLong ContextProductionGoogleAnthropicOpenAI