Two forces shape this design: documents longer than the context window, and summaries that must not lie. The strong answer pairs hierarchical map-reduce with claim-level grounding, then serves it cheaply at scale.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Two forces shape this design: documents longer than the context window, and summaries that must not lie. The strong answer pairs hierarchical map-reduce with claim-level grounding, then serves it cheaply at scale.
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.
