---
id: "action-implement-human-validation"
type: "action-item"
source_timestamps: ["00:15:30", "00:15:42"]
tags: ["data-engineering", "quality-assurance"]
related: ["concept-production-trust", "framework-data-migration-pipeline", "question-backend-hygiene"]
action: "Require human-in-the-loop validation and systemic checks for all AI data migrations."
outcome: "Prevention of backend hygiene failures and corrupted production databases."
speakers: ["Nate B. Jones"]
sources: ["s26-gpt55-claude-gemini"]
sourceVaultSlug: "s26-gpt55-claude-gemini"
originDay: 26
---
# Implement Human Validation for Data

## Action
**Do not trust any model with one-shot database migrations.** Build a system around the model that includes:
- **Row count checks** at every stage.
- **Enum map inspections** before any merge step.
- **Human-approved canonical merge** step before pushing to production.
- **Audit UI** as the final gate (see step 5 of [[framework-data-migration-pipeline]]).

## Why
Even [[entity-gpt-5-5|GPT-5.5]], the strongest model on adversarial trap detection, still fails at boring backend hygiene like enum normalization and service code preservation. See [[concept-production-trust]] and the open question [[question-backend-hygiene]].

## Expected Outcome
Prevention of backend hygiene failures and corrupted production databases.

## Implementation Tip
Use the LLM to **write deterministic validation code** rather than to *be* the validator. This converts a probabilistic step into a verifiable one — and is one possible resolution path to [[question-backend-hygiene]].