BuildMidData / MLAuto-graded

Extract valid JSON from messy LLM replies

Assesses defensive handling of unreliable AI output, including the judgement of when to stop and fail cleanly.You are wiring an LLM into a pipeline that needs a real dictionary. The model mostly returns JSON, but real output is messy: wrapped in prose, fenced in a code block, or carrying the small syntax slips models make — trailing commas, single-quoted keys. A bare JSON parse falls over on nearly all of it. Build the tolerant parser that turns messy model output into usable data, and fails loudly when there is genuinely nothing valid in it.

30 min

python

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You're done when

JSON wrapped in surrounding prose is extracted and parsed.
JSON inside a fenced code block is parsed, with the fence handled.
Common model syntax slips — trailing commas, single quotes — are tolerated.

What it measures

Generative AIMaking model output machine-usable, which is the step between a working prompt and a working system.

Prompt engineeringKnowing the specific ways models deviate from a requested format, and which are worth tolerating.

Failing loudlyDistinguishing recoverable mess from genuinely absent data, so a silent empty result never reaches the pipeline.

Your environment

Terminal + editorA full shell in the browser

Claude CodePre-authenticated, no key setup

Isolated microVMYours alone, wiped afterwards

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30 min

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