ARF Layer
AI Readiness & Interoperability
Ensures the model is AI‑readable, well‑governed, and compatible with different AI tools.
Layman explanation
This layer makes your model usable by AI systems without guessing. It covers metadata density, semantic contracts, retrieval patterns, and governance. The focus is tool‑agnostic: a robust model works across LLMs and BI tools.
A loop from metadata to retrieval context to LLM answer to evaluation.
What breaks when this layer is weak
- • AI produces confident but wrong answers due to missing context.
- • Different LLMs give incompatible results from the same model.
- • Security or privacy boundaries are unintentionally crossed.
Symptoms you can observe
- • Sparse descriptions for tables and measures.
- • No formal “contract” for valid questions.
- • Inconsistent data access across tools.
- • Lack of evaluation or monitoring.
Root causes
- • Metadata is treated as optional.
- • No governance for semantic changes.
- • Retrieval context is incomplete or stale.
- • Security policies are not tested for AI flows.
What good looks like
- • High metadata density across the model.
- • Clear semantic contract for queries and answers.
- • Standard retrieval patterns that supply the right context.
- • Ongoing evaluation with measurable progress.
Remediation checklist
- • Add descriptions and annotations at table, column, and measure levels.
- • Define a semantic contract for key metrics and dimensions.
- • Implement evaluation queries and regression tests.
- • Document and enforce security boundaries.
Metrics to track
- • Metadata coverage %
- • # of validated semantic contracts
- • AI answer accuracy vs gold set
- • Security audit pass rate
Foundational articles
AI-Readable Schemas: What It Means in Practice
AI‑readable schemas have clear names, relationships, and metadata.
Metadata Density: Why Descriptions Matter More Than You Think
Metadata density makes models interpretable by AI and humans.
Semantic Contracts: Setting Expectations for Questions and Answers
Semantic contracts define what questions are valid and how answers should be interpreted.
Grounding: Preventing Confidently Wrong Answers
Grounding anchors AI answers in model facts and metadata.
Retrieval Patterns for BI: Getting the Right Context to the Model
Retrieval patterns define which metadata and filters should be provided to AI.
Prompting vs Modeling: Where to Fix the Problem
Most AI answer issues are model issues, not prompting issues.
Tooling Interfaces: SQL, DAX, and the Translation Layer
Different tools expose different query layers; AI must align with them.
Governance for AI Analytics: Change Control for Semantics
Governance ensures semantic changes are intentional and traceable.
Evaluation: How to Test AI Answers Against Your Model
Evaluation compares AI answers against expected model outputs to detect errors.
Security and Privacy: What Not to Expose
AI access must respect security boundaries and avoid exposing sensitive data.
Interoperability: Aligning Power BI With the Rest of Your Stack
Interoperability ensures consistent semantics across BI, data platforms, and AI tools.
An AI Readiness Scorecard You Can Run Monthly
A simple scorecard tracks progress across metadata, context, and explainability.