AI Readiness & Interoperability
Prompting vs Modeling: Where to Fix the Problem
Most AI answer issues are model issues, not prompting issues.
TL;DR
- • Prompting can’t fix ambiguous data models.
- • Fix the model first, then refine prompts.
The problem (layman)
- • Teams try to patch model issues with prompts.
- • AI still returns inconsistent answers.
Why it matters
- • Prompts are fragile; model changes are durable.
- • Model fixes improve all tools at once.
Symptoms
- • Prompt tweaks help one question but break another.
- • AI still struggles with definitions.
Root causes
- • Ambiguous measures and weak metadata.
- • Lack of semantic contracts.
What good looks like
- • Model definitions are clear, prompts are simple.
- • Prompting is used for formatting, not semantics.
How to fix (steps)
- • Identify root model issues.
- • Improve definitions, metadata, and context stability.
- • Use prompts for output structure only.
Pitfalls
- • Over‑engineering prompts as a substitute for model fixes.
- • Ignoring evaluation results.
Checklist
- • Model issues addressed first.
- • Prompting used for formatting.
- • Evaluation shows improved consistency.