Schema Validation

Even with careful prompting or structured generation, production systems should validate LLM output against the expected schema before using it — treating the model's output as untrusted input to your own system, not a guaranteed-correct value.

Basic Validation Example

from jsonschema import validate, ValidationError

schema = {
    "type": "object",
    "properties": {
        "order_id": {"type": "string"},
        "status": {"type": "string", "enum": ["pending", "shipped", "delivered"]},
        "total": {"type": "number"}
    },
    "required": ["order_id", "status", "total"]
}

def get_validated_response(raw_json_string):
    try:
        data = json.loads(raw_json_string)
        validate(instance=data, schema=schema)
        return data
    except (json.JSONDecodeError, ValidationError) as e:
        log_warning(f"Invalid LLM output: {e}")
        return None  # caller must handle this explicitly

Two distinct failure modes are worth catching separately: invalid JSON syntax (json.JSONDecodeError) and valid JSON that doesn't match your schema (ValidationError, e.g. a missing required field or wrong type).

What to Do When Validation Fails

Why This Step Is Not Optional

Skipping validation means any structural drift in model output — a missing field, an unexpected type, a value outside the expected enum — flows directly into your application logic, potentially causing a crash, silent data corruption, or an incorrect action taken automatically. Validation is the boundary that catches this before it propagates.

Practical Use Case

Any pipeline where LLM output feeds directly into automated downstream logic (updating a database, triggering a workflow, calling another API) needs validation as a hard gate — this is standard defensive engineering, applied to a new kind of untrusted input source.

Common Mistakes

• Trusting LLM output structure implicitly because "it usually works," without a validation step catching the cases where it doesn't
• Treating "valid JSON" and "matches my expected schema" as the same check — valid JSON can still have the wrong fields or types
• Crashing the whole request pipeline on a validation failure instead of a graceful, handled fallback path

Interview Relevance

"Should you trust an LLM's structured output without validation, even with a well-designed prompt?" — no; a strong answer treats LLM output as untrusted input needing the same validation discipline as any external data source.

Practice Question

Design the validation and fallback logic for a feature that extracts a shipping address from customer text — what happens if a required field is missing?

Related Notes

• JSON Schema
• API Error Handling
• LLM Fallbacks