Transparency in AI decisions

AI explanations help you understand why the assistant made specific suggestions or applied certain filters. Instead of guessing how the AI reached a conclusion, you can see the logic behind its choices. This builds trust and helps you refine your requests for better results.

Where to find AI explanations

You’ll see explanations in two main formats depending on the context:

Tooltip explanations

In compact views like the Filter Bar, look for the AI Explanation icon (a small info circle with sparkles). Hover over this icon to see a brief summary of the AI’s reasoning.

Block explanations

In detailed workflows like Data Mapping or Import Preview, explanations appear as a dedicated block. These sections provide a more thorough breakdown of the logic used to process your data.

Understanding AI reasoning

Each AI Explanation typically covers:

  • Input interpretation: How the assistant understood your natural language request.
  • Logic applied: The specific rules or patterns the AI used to generate the result.
  • Confidence: Why the AI chose one option over another when multiple possibilities existed.

When explanations are available

Explanations are automatically generated for most AI-powered features, including:

  • AI Filtering: Understanding how your text was turned into a database query.
  • AI Mapping: Seeing why specific source columns were matched to system fields.
  • Smart Suggestions: Learning why the assistant recommended a particular action.

Tips for using explanations

**Tip:** If an explanation shows the AI misunderstood your intent, try rephrasing your request using the specific field names mentioned in the explanation.

**Note:** AI explanations are generated in real-time. If you modify a generated filter manually, the explanation might no longer apply to your custom changes.