# Understanding Query Logic in BI Genius

BI Genius is not a black box. While it leverages powerful AI to understand and respond to user questions, every step of the process is **explainable, auditable, and grounded in your data**.

This article explains how BI Genius handles user queries, what happens behind the scenes, and how we prioritize transparency in every interaction.

## What is Query Logic?

In BI Genius, ***query logic*** refers to the step-by-step process the system follows when a user asks a question—transforming natural language into an accurate, data-driven response.

This process involves:

1. **Understanding the user’s intent**
2. **Mapping the request to your data model**
3. **Constructing a DAX, SQL query or structured explanation**
4. **Returning the result with contextual reasoning**

Every one of these steps is traceable and explainable—by design.

## The Basic Query Flow

Here’s a simplified breakdown of how BI Genius processes a query:

#### 1. **Intent Parsing (AI Layer)**

* The user types a natural language question (e.g., “How did sales perform last quarter?”).
* Azure OpenAI interprets the request, identifies relevant metrics, dimensions, and time filters.

#### 2. **Context Assembly**

* BI Genius references your Power BI Semantic Model to locate the appropriate tables, measures, and filters.
* Optional external knowledge (e.g., glossary terms, documentation) may be used to disambiguate or enrich the query.

#### 3. **Query Generation**

* BI Genius builds a **DAX query** (or narrative logic) tailored to your model.
* This query is assembled transparently—you can view and audit the logic used.

#### 4. **Execution and Response**

* The query is executed against your dataset via XMLA or REST APIs.
* The response is returned to the user—optionally with a **plain-language explanation of how the result was calculated**.

***

### 🔍 Example

**User Prompt:**

> “What were the top 5 regions by profit last year?”

**BI Genius Explanation (visible to user):**

> “I calculated this by filtering your ‘Profit’ measure by last calendar year, then sorting by region and returning the top 5 results.”

**Technical View:**

```dax
DAX Expression

TOPN(5, 
     SUMMARIZE('Sales', 'Region'[Name], "Profit", [Total Profit]), 
     [Total Profit], DESC)
```

## Why Explainability Matters

Transparency builds trust, especially when AI is involved in data interpretation. BI Genius was built with explainability in mind to ensure:

**Accuracy** — users can verify the logic used in a response

**Trust** — especially in regulated industries or critical decision workflows

**Learning** — users grow more confident in both BI Genius and the underlying data

**Compliance** — audits are supported with traceable, interpretable query steps

## Customization & Control

* You can configure whether users see **just the answer**, or the underlying **query logic breakdown**.
* **Query Audit Logs** for Admins: View historical query chains and logic trees for traceability and providing troubleshooting assistance. 


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.thereportinghub.com/understanding-query-logic-in-bi-genius.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.