GIGO (Garbage In, Garbage Out)
The foundational data rule: inaccurate or duplicate input data produces inaccurate AI outputs, regardless of model sophistication.
Plain Language Definition
GIGO is the fundamental warning in data management: if you load messy, duplicate, or wrong facts into your system, the AI will generate wrong answers — no matter how sophisticated the AI model itself is. Poor-quality training data, unresolved entity duplicates, and conflicting records do not get cleaned up by the model. They become the basis for confident, incorrect outputs.
Technical Definition
Metric degradation principle in graph database and ML pipeline contexts where duplicate nodes, inconsistent attributes, or contradictory facts propagate through retrieval and generation pipelines — producing false relationship paths in graphs and hallucination-prone LLM outputs.
Why This Matters for AI Search Visibility
The most capable AI model cannot produce accurate answers from inaccurate source data. Businesses with inconsistent directory listings, conflicting NAP data, and unresolved duplicate records will receive inaccurate AI-generated information about themselves — and so will their customers.