AI Readiness & Interoperability
Grounding: Preventing Confidently Wrong Answers
Grounding anchors AI answers in model facts and metadata.
TL;DR
- • Grounding reduces hallucinations.
- • Provide the model with the right context.
The problem (layman)
- • AI answers without enough context can be wrong but confident.
- • Missing metadata leads to guesswork.
Why it matters
- • Ungrounded answers are dangerous in decision contexts.
- • Trust depends on verified context.
Symptoms
- • AI cites metrics that don’t exist.
- • Answers use the wrong filters or time periods.
Root causes
- • Sparse metadata and weak retrieval patterns.
- • No validation against the model.
What good looks like
- • Answers reference explicit model metadata.
- • Grounding data is included in every response.
How to fix (steps)
- • Improve metadata density.
- • Define retrieval patterns that include key context.
- • Validate AI answers against the model.
Pitfalls
- • Assuming AI will infer missing context.
- • No error handling for missing data.
Checklist
- • Grounding context included in responses.
- • Metadata coverage improved.
- • Answer validation in place.