chunking

Everyone says '500 tokens with overlap.' The candidates who get hired explain why chunk boundaries are a recall problem, name three strategies, and describe the experiment that picks between them.

A reported FDE design question that punishes one-size-fits-all answers. Each corpus has a different 'semantic unit': get the three designs, the parsing tools to name, and the eval that proves the split was worth it.

The eval set says recall fell 2.3 points, which is inside the noise you would expect from a re-index. Support says the product got worse. Both are right, and the reason is that one document class lost 49 points of recall while everything else improved slightly.

The coding question that doubles as a RAG-fundamentals check. Greedy packing is easy; the oversized-sentence case, the overlap-progress trap, and the tests you write unprompted are what AI-lab interviewers actually score.