Your prompts are enormous and repetitive. How do you compress context without losing accuracy?
Most teams reach for a token-dropping compressor and pay an accuracy tax for tokens they could have had for almost nothing. The ranking of levers matters more than any single technique, and the top of the list is not compression at all.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Most teams reach for a token-dropping compressor and pay an accuracy tax for tokens they could have had for almost nothing. The ranking of levers matters more than any single technique, and the top of the list is not compression at all.
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.
