> ## Documentation Index
> Fetch the complete documentation index at: https://docs.clairelabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# How to gain Insights
> Learn how to query data and analyze performance using the Insights engine.
To truly understand learning progress and outcomes on a course or assessment-level requires more than just AI feedback tools. It requires the ability to query data and generate insights.
Claire enables educators to query student performance and feedback data conversationally, providing valuable insights into course performance, blind spots, ambiguity, and other signals of learning success or struggles.
Navigate to Insights via the left sidebar to access the chat interface.
Use natural language to ask direct questions about class performance.
For example, you can ask: *"What was the most common mistake students made on the second rubric criterion?"* or *"Summarize the strengths of the top 5 submissions."*
Claire will query your submission data, grading remarks, and rubric criteria to provide a synthesized answer, helping you spot trends across the entire class.
Use these insights to quickly adjust your teaching strategy, address common misconceptions in your next lecture, or refine your assessment instructions without manually reading through every single grading report.
## Data in, data out
The conversational interface relies on the data generated during the review and grading phases. The more detailed your annotations and grading remarks are, the richer and more accurate the insights will be.
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**Looking for more details?**
* To learn more about querying your course data, see [Insights](/documentation/claire-ai/insights).
* To generate a class-level report summarizing performance, see [Course reporting](/grading/class-performance-reports).