Verify AI-generated library calls
An AI assistant wrote you a small Python script that calls a library function `requests.get_json(url)` to fetch and parse JSON from an API in one step. It looks convenient and reads naturally. Before you build on it, how do you confirm that method actually exists and behaves the way the code assumes?
Implement
unverified_api_calls(known_methods: list[str], calls: list[str]) → list[str]Examples
in
[["requests.get","requests.post","Response.json"],["requests.get","requests.get_json"]]out["requests.get_json"]in
[["requests.get","Response.json"],["requests.get"]]out[]What a strong answer looks like
Treat the AI’s output as a draft to verify, not an answer to trust. Name the specific flaw and the input that triggers it, say how you’d catch it (tests, edge cases, reading critically), and how you’d re-prompt or decompose to get it right.
0:00 of about 12 min
Vibe & agentic: describe the solution in plain language (or narrate it) and the coach grades your approach.
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.