Integrating LLMs with structured output simplifies backend development by removing the need for manual JSON parsing and error handling. However, structural validity does not guarantee semantic accuracy. Developers often mistakenly treat LLM output as a verified data source immediately after deserialization, ignoring potential logical errors in the extracted content.
To ensure reliability, the author proposes treating LLM output as untrusted data, similar to external API inputs. Validation should occur in stages: schema validation, domain-specific checks, and evidence verification. For instance, cross-field validation, such as checking if subtotal plus tax equals the total, should be handled by deterministic code rather than relying on the LLM to perform calculations.
Provenance is critical for high-stakes tasks. Requesting evidence—such as specific text snippets or character ranges from the original document—allows the backend to verify the model's claims. Relying on secondary LLMs for verification can be costly and prone to similar errors, while confidence scores often lack statistical calibration and require separate evaluation datasets.
A robust production pipeline should classify LLM results as candidates that only become accepted after passing deterministic validation rules. Monitoring metrics like schema validity, domain validity, and evidence failure rates provides better visibility into system health than tracking HTTP errors alone. Maintaining a test set of real-world examples is essential for validating changes to prompts or models.
