Fast Facts
- Structured Outputs ensure JSON schema validity but don’t guarantee data accuracy—missing fields often get defaulted (like dates filling in with the current date), leading to subtle inconsistencies.
- Making schema fields nullable allows the model to admit missing info, shifting the trust from the model to your own validation, and enabling detection of missing or hallucinated data.
- Incorporating evidence fields that quote source snippets helps verify if a value was truly extracted from the text, revealing hallucinations but increasing token costs and complexity.
- Combining schema validation with custom validators and retry logic ensures data makes sense (like positive amounts or plausible dates), acting as a final gatekeeper beyond just shape and JSON correctness.
Perfect JSON Doesn’t Mean Perfect Data
Many believe that if their language model (LLM) produces valid JSON, it is automatically reliable. However, that’s not always true. For example, I used structured outputs to parse payment messages. At first, everything seemed fine because the JSON was well-formed. Yet, subtle errors crept in. About 2 to 3 percent of transactions had wrong dates. The model filled in dates by default, often with the extraction time. So, perfect JSON doesn’t guarantee correct or truthful data. It simply means the output follows the right format. This shows that validation of shape isn’t enough. We need to check the actual content. When real-world facts are involved, correctness depends on more than just the syntax.
Handling Uncertainty with Schemas
Designing schemas thoughtfully helps address these issues. Previously, extracting data from language models required complicated workarounds like regexes. Now, with native schema enforcement, most of that pain vanishes. Using tools like Pydantic, you can set clear rules for your data. For example, you can make fields nullable. Instead of forcing the model to fill every slot, you let it say “I don’t know.” This approach reduces false confidence and more accurately reflects what the model truly understands. It separates extraction from inference, empowering the code to handle missing or uncertain information directly. This method makes your system more honest and adaptable.
From Shape to Truth: Adding Validation
While schemas and nullable fields improve reliability, they don’t fully solve the problem. Sometimes, the model outputs a value that looks right but isn’t accurate. For example, it might say a transaction date is today, even if the message provides no date at all. To prevent this, adding validation checks becomes essential. Using validators, the code can verify if the data makes sense — not just if it’s valid JSON. Checks like “amount must be positive” or “date can’t be in the future” catch errors early. Moreover, implementing retries allows the system to ask the model to fix its answers. This combination of validation and retries shifts responsibility from the model to the system, ensuring higher trustworthiness. Ultimately, reliable data depends on verifying not only format but also factual consistency.
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