Data Quality for AI Systems
Garbage in, garbage out is amplified with LLMs. Learn to build automated data quality pipelines that detect near-duplicates, track freshness, measure coverage gaps, and score completeness — so your agent never confidently serves stale or incorrect information.
Quick Reference
- →Data quality directly determines AI output quality — a 10% improvement in data quality often beats a model upgrade
- →Near-duplicate detection: MinHash LSH catches 85%+ similar documents that exact hash misses
- →Freshness SLA: define maximum age per source type (API docs: 24h, legal: 7d, blog posts: 30d)
- →Coverage measurement: compare user queries against indexed topics to find knowledge gaps
- →Automated quality scoring: check completeness, consistency, readability, and metadata presence
Why Data Quality Is the Highest-Leverage Investment
Teams spend weeks tuning prompts and evaluating models when the root cause of bad answers is bad data. In production RAG systems, 60-70% of incorrect responses trace back to data quality issues: stale content, missing information, or duplicated contradictory documents.
| Quality Issue | User Impact | Detection Method | Frequency in Production |
|---|---|---|---|
| Stale content | Agent gives outdated answers with high confidence | Freshness tracking with source timestamps | Very common — #1 issue |
| Near-duplicates | Retriever returns redundant chunks, wastes context window | MinHash LSH similarity detection | Common in wiki-based KBs |
| Incomplete docs | Agent says 'I don't have enough information' or hallucinates | Completeness scoring against templates | Common after migrations |
| Contradictory sources | Agent gives different answers to the same question | Cross-document consistency checks | Common with multiple authors |
| Format corruption | Retriever matches on boilerplate, not content | Structural validation rules | Common with PDF extraction |
The most insidious quality issue is contradiction between sources. When two documents disagree, the retriever may return either one depending on the query phrasing, creating an agent that gives inconsistent answers. Users lose trust fast when the same question gets different responses.