Retrieval Patterns for BI: Getting the Right Context to the Model

TL;DR

• Retrieval is about context selection.
• Good patterns reduce ambiguity.

The problem

• 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

• 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.

Framework placement

Primary ARF layer: AI Readiness & Interoperability. Diagnostic bridge: data-movement-reliability, semantic-reliability, execution-reliability, change-reliability.