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AI Engineering with Python
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Module 6 · Frontier AI Engineering
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Long context: 1M window strategies
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📝 **Question:** When is 1M context the right choice? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
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📝 **Question:** When is 1M context the right choice? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
A
Always — dump every document, transcript, and code file you have so the model never misses anything during synthesis
B
When you need cross-document reasoning across a fixed, mostly-relevant corpus, and you can amortize the cost via prompt caching.
C
Only for marketing demos and screenshots — production traffic never justifies the prefill latency cost of a full 1M-token window
D
Never — RAG is strictly cheaper and higher quality, and the 1M window exists only as a fallback for users who cannot run a vector store
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