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Data Quality for AI

Summary​

Data quality for AI is not just a data engineering concern. It is a product reliability concern. This page shows how to define quality standards that protect retrieval systems, feature pipelines, and model-driven user experiences.

Why This Matters​

  • GenAI output quality is directly coupled to source quality, freshness, and consistency.
  • Poor quality controls increase hallucinations, bad recommendations, and customer-facing defects.
  • Clear quality ownership enables faster incident resolution and safer release velocity.

Core Concepts​

  • Data contracts: define required fields, allowed values, and schema evolution rules.
  • Data quality dimensions: completeness, accuracy, timeliness, consistency, and traceability.
  • Release gates: block model or index promotion when quality thresholds fail.
  • Feedback loops: route runtime defects back to data producers with measurable remediation SLAs.

Use the flow above to sequence decisions for Data Quality for AI before implementation starts.

Diagram​

Implementation Steps​

  1. Identify the top 3 AI journeys where bad data causes business impact.
  2. Define data contracts for each critical dataset used by prompts, retrieval, or features.
  3. Set measurable quality SLOs (for example: freshness under 4 hours, null rate under 0.5%).
  4. Enforce quality checks in ingestion and before index/model release.
  5. Add runbooks that map each failed quality rule to an owner and response time.

Realistic Example​

A support copilot used a knowledge index built from ticket articles. Answer accuracy dropped after a content migration introduced malformed metadata. The platform team added schema validation, freshness alerts, and index promotion gates. Within two sprints, escalation rate dropped by 22 percent and time-to-resolution improved.

Senior Tech vs Dev Conversation​

Senior Tech: Why do quality programs fail even with good dashboards? Dev: Because they measure quality but do not enforce release consequences. Senior Tech: What changes behavior? Dev: Contract violations must block promotion and auto-create remediation work.

UX/UI Checklist​

  • Show quality scorecards by dataset, owner, and downstream AI dependency.
  • Expose freshness drift and schema break alerts with impact labels.
  • Provide one-click trace from bad output to source dataset lineage.
  • Display remediation SLA timers and current responder.

Common Pitfalls​

  • Relying on periodic audits instead of continuous quality enforcement.
  • Indexing data before semantic normalization and metadata validation.
  • Treating data defects as application bugs with no producer accountability.

References and Next Steps​

  • Pair this page with Lakehouse Architecture and Data Catalog and Lineage.
  • Define one enterprise quality standard package for all AI products.
  • Start with a 30-day pilot on one high-impact assistant and publish before/after metrics.