1. Moving Beyond Eyeball Testing
Prototyping an LLM application in a playground takes an afternoon. Bringing it into mission-critical customer operations requires rigorous, automated regression validation.
When model providers update their weights or you adjust system prompts, subtle regressions occur in tone, JSON adherence, or domain precision. You need continuous CI benchmark suites to catch these before deployment.
2. The Three Pillars of Production AI Evaluation
We structure evaluation around three strict layers: Schema Determinism (Zod / Pydantic validation), Grounded Faithfulness (measuring whether output facts exist in retrieved context), and Answer Relevance (measuring intent fulfillment).
python
from pydantic import BaseModel, Field
from typing import List
class ExtractionResponse(BaseModel):
account_id: str = Field(pattern=r"^ACC-\d{6}$")
risk_score: float = Field(ge=0.0, le=1.0)
flagged_entities: List[str]
rationale: str = Field(min_length=20, max_length=500)
def validate_llm_output(raw_json: str) -> ExtractionResponse:
# Strict deterministic parsing - fails CI if schema is violated
return ExtractionResponse.model_validate_json(raw_json)Key Architectural Invariants
- Enforce JSON schema boundaries with Pydantic or Zod at the boundary.
- Implement cross-encoder semantic scoring against reference ground truth.
- Automate evaluation runs as blocking steps in GitHub Actions CI.
Written By
Aditya Chaurasia
Principal Architect at DigantaLabs