Part 1: Why Copilot Guesses: It is Not a Bug

The behavior described above is not a software error. It is a basic feature of the large language models (LLMs) that power Copilot. These models are probabilistic; they are built to generate responses, not to perform exact calculations like a calculator. When given an ambiguous prompt, the AI must guess.

Non-Determinism: The Core Trait

Generative AI does not “understand” your business context. It only sees the prompt and the data model’s structure (its schema). When a user asks for “gross profit,” the AI scans the schema for matching column names. If it finds both [GrossProfit] and [GrossProfitper], it sees two good options. Without more context, its choice is a statistical guess. This means the same prompt might even give different results at different times. Business intelligence tools demand consistent, verifiable answers. This AI behavior conflicts with that need.

The NLQ-to-DAX Pipeline Failure

Here is what happens in the background when you type a prompt:

1. Execution: Power BI runs this bad query and shows you a visual you did not want.

A Deeper Look: The “Grounding” Process

The “Grounding” step is the most critical phase. This is where Copilot forms its context. It’s not just reading the column names. It actively scans and prioritizes several layers of your model’s metadata:

• Field Descriptions: This is a high-priority item. A well-written description is a direct instruction to the AI.
• Synonyms: Newer Power BI features allow you to define synonyms for fields. For example, you can link “Revenue,” “Income,” and “Turnover” to your [Total Sales] measure.
• Data Types & Categories: It knows [Date] is a date, [City] is a location (and can be mapped), and [Profit] is a number.
• Model Relationships: It understands how tables are linked. This is how it knows to connect [ProductCategoryName] from the ‘Products’ table to the [SalesAmount] from the ‘Sales’ table.

The failure occurs when this grounding process still results in ambiguity. If two fields (like [GrossProfit] and [GrossProfitper]) both seem like equally good matches, the AI is forced to guess.

The failure is not in the AI’s logic. The AI *correctly* reported the ambiguity it found in the data model. To fix the output, you must first fix the input. This can be done by the user (by writing a better prompt) or by the developer (by building a better model).

Part 3: The Developer-Side Fix: Build an AI-Ready Model

Relying on every user to write perfect prompts is not a scalable solution. The better, long-term fix is for Power BI developers to design data models that are “AI-ready.” This means removing ambiguity before the user ever types a prompt. Microsoft provides a set of tools in Power BI Desktop and Fabric to do this.

Layer 3 Fix: Guide with AI Instructions

This tool provides a text box where you can give plain-English rules to the AI. You can write: “GrossProfit is the total dollar amount. GrossProfitper is the percentage. If a user just asks for ‘gross profit,’ they always mean the [GrossProfit] measure.” This instruction is added to the AI’s context, guiding it to the right choice.

A Developer’s Best Practice Checklist

Use this checklist to create a robust, AI-ready data model. This moves your model from being ambiguous to being deterministic.

Part 4: A Strategic Framework for Ambiguity

The solutions above show that developers and users must work together. Users should be specific, and developers should build clear models. The following table compares all mitigation techniques.

Mitigation Strategy Comparison Matrix

Conceptual: Prompt Reliability

This chart illustrates how prompt specificity impacts reliability. A vague prompt has a high chance of an ambiguous, “guessed” answer. A specific, command-style prompt forces a single, correct answer.

The Business Cost of Ambiguity

This is not just a technical problem. Inconsistent or incorrect answers from an AI tool have direct business costs:

Part 5: Advanced Scenarios and Synonyms

As you gain control over basic ambiguity, you will encounter more complex semantic challenges. How you handle these will determine the true intelligence of your data model.

Handling Synonyms: “Sales” vs. “Revenue” vs. “Income”

To your business, “Sales,” “Revenue,” and “Income” might mean the same thing. To the AI, they are just different text strings. If you have a measure called [Total Sales], a user asking “Show me total revenue” will get a “field not found” error.

The Solution: You must explicitly map these terms. In the Power BI model view, you can select the [Total Sales] measure and add “Revenue,” “Total Revenue,” and “Income” to its list of synonyms. This teaches the AI that these words all point to the same, single measure. Now, all three prompts will return the correct visual.

The Multi-Lingual Challenge

What happens when your model is in English, but your users are global? A user in France might ask for “ventes par catégorie” (sales by category). Copilot may fail if your model only contains “Sales” and “Category.”

The Solution: This requires a more advanced setup. You can use translations and add multi-lingual synonyms to your model. You would add “ventes” as a synonym for [Total Sales] and “catégorie” for [ProductCategoryName]. This makes the model accessible to a global user base without changing the underlying data structure.

When Ambiguity is (Briefly) Useful

There is one situation where ambiguity is not a problem: pure exploration. A user prompt like “Show me what’s interesting in my sales data” is intentionally vague. This invites the AI to be “creative” and generate several charts (e.g., sales over time, sales by region, sales by product) to find an interesting pattern.

However, this is *exploratory analysis*, not *reporting*. The moment a user needs a specific answer to a specific question (“What was our profit last quarter?”), ambiguity becomes a liability. Your goal as a developer should be to eliminate ambiguity for all known business questions, while still allowing for broad exploration.

Part 6: Interactive Prompt Builder Tool

This tool helps you translate your desired DAX query into a reliable, unambiguous Copilot prompt. By thinking about the exact DAX query you need, you can learn to write prompts that remove all guesswork for the AI. Write the DAX you want, and see the perfect prompt to get it.

Copy
...

Frequently Asked Questions (FAQ)