A bloated semantic model (full of helper measures, intermediate objects, and cryptic field names) creates confusion for the LLM. AI data schemas let you define a focused, prioritized subset of your model specifically for AI consumption. Select only what the agent needs. Name every field in plain English.
Select only the tables exposed in your AI data schema when configuring the Fabric data agent. Rename cryptic fields to business terms: TR_AMT → Total Revenue eliminates an entire class of misinterpretation errors at source.
A user asks "What were our sales last quarter?" If the model exposes Gross Sales, Net Sales, and Total Revenue, the agent may arbitrarily choose the wrong one. Scoping the AI schema to a single preferred measure removes the guesswork entirely.
Verified answers are user-approved visual responses bound to specific natural language triggers. When activated, the agent uses the visual's underlying columns, measures, and filters as guardrails to generate the correct DAX query, not a guess. This is your primary control for high-stakes questions that must return a specific, trusted result.
Configure 5–7 trigger questions per verified answer, mixing conversational and formal phrasing. Avoid relying on hidden columns; verified answers will silently fail if the model obscures a column they reference.
A user asks "Show me performance by territory." Without a verified answer, the agent may query a Products table's Territory column instead of the regional sales dimension. Binding the question to the correct regional visual fixes the mapping permanently.
AI instructions allow unstructured business logic (definitions, time period conventions, metric preferences, default groupings) to be embedded directly in the semantic model. This is how you bridge organizational jargon and the LLM's generic reasoning. Keep them specific, non-conflicting, and concise.
Keep instructions specific and non-conflicting. Conflicting rules increase latency and produce unpredictable outputs. Define time periods (e.g., "Peak season is Nov–Jan"), preferred metrics, and default groupings as concise, unambiguous statements.
A manager asks "Who were the top performers last month?" The LLM has no native concept of what "top performer" means in your organization. An AI instruction stating the quota attainment threshold and the correct table to filter against translates the abstract question into a precise, filtered DAX query.