Use consistent, typed exceptions at the point of failure, with structured context, and never rely on unchecked/bypassed paths that convert errors into later crashes. Apply: - Credential/auth failures: raise your library’s canonical error (e.g., `OpenAIError`) for all credential/installation/mode-resolution issues; wrap underlying exceptions via `raise ... from exc`. - Parsing/validation failures: when decoding/validating responses, catch schema/JSON errors and raise a dedicated typed error (e.g., `ContentFormatError`) that includes actionable context (such as `raw_content`). - Polling/loops: always enforce timeouts/terminal-state handling; if the loop ends due to timeout or failure, raise a clear exception immediately (avoid letting later code assume fields exist). - Exception chaining: prefer `raise NewError(...) from exc` and don’t manually set `__cause__`. Example (parsing boundary): ```python try: model_parse_json(MyModel, content) # or TypeAdapter(...).validate_json except (pydantic.ValidationError, json.JSONDecodeError) as exc: raise ContentFormatError(raw_content=content, error=exc) from exc ``` Example (polling boundary): ```python start = time.time() while not until(response): if time.time() - start > TIMEOUT_SECS: raise OpenAIError("Polling timed out") response = self.request(...)[0] ``` This reduces mystery failures (like `KeyError`) and makes errors actionable for users and tests.