parsing

Before you touch a model, check whether the two numbers measure the same thing. Field accuracy of 96% across twelve fields is 61% of documents fully correct, which means half of the drop may be arithmetic. The other half is the documents the pilot never saw.

The quintessential FDE practical: messy real-world input, grouping, ranking, and a hidden rubric line for how you treat malformed data. Most candidates parse happily and lose the round on the lines that don't parse.

A three-level build that quietly tests whether your L1 design survives L3. Regex substitution wins level one and loses the round; here's the token-based structure that carries through conditionals and loops.

The practical build that's secretly a graph problem: formulas are a dependency DAG, evaluation is DFS with memoization, and the three-color cycle trick decides whether A1=B1, B1=A1 crashes you or earns the offer.

The build behind every access policy and routing rule a customer wants to edit without a deploy. The trap is operator precedence: candidates who tokenize fine still get `a or b and c` wrong, and that's the whole grade.

A classic FDE take-home shape: messy real documents in, structured index out. The grade is in the seams between reader, extractor, and writer, and in what happens when one file is garbage.

Real LLM logs are JSONL, not JSON, and real log files have a malformed line halfway through. The warm-up that screens whether you can stream a file and aggregate without one bad record nuking the whole run.

BPE is how GPT and most LLMs turn text into tokens, and building a tiny trainable version from a corpus is a favorite deep-dive. The screen for whether you understand the merge loop, deterministic tie-breaking, and round-trip decode, not just tiktoken.encode.