AI Readiness & Interoperability
Retrieval Patterns for BI: Getting the Right Context to the Model
Retrieval patterns define which metadata and filters should be provided to AI.
Rendering diagram...
Question leads to context bundle, LLM, and answer with evidence.
TL;DR
- • Retrieval is about context selection.
- • Good patterns reduce ambiguity.
The problem (layman)
- • AI responses lack the context needed for correct answers.
- • Different tools provide different context sets.
Why it matters
- • Consistent retrieval improves accuracy across AI systems.
- • It reduces variability between runs.
Symptoms
- • AI answers change depending on the tool.
- • Explanations omit important constraints.
Root causes
- • No standard retrieval pattern.
- • Context selection done ad hoc.
What good looks like
- • Defined context bundle for each KPI.
- • Consistent retrieval across tools.
How to fix (steps)
- • Define standard context fields per KPI.
- • Include filters, units, and definitions.
- • Document retrieval patterns and enforce them.
Pitfalls
- • Providing too much context, causing noise.
- • Providing too little context, causing guesswork.
Checklist
- • Retrieval patterns documented.
- • Context bundles tested.
- • Consistency across tools validated.