How do you evaluate long-context models (needle-in-a-haystack and beyond)?
A model that claims a million-token window may not actually exploit it. The signal is knowing needle-in-a-haystack, where it fails, and how to probe genuine multi-fact reasoning across the entire context. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A model that claims a million-token window may not actually exploit it. The signal is knowing needle-in-a-haystack, where it fails, and how to probe genuine multi-fact reasoning across the entire context. Here is the answer.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
