> For the complete documentation index, see [llms.txt](https://docs.rillet.com/EjmEP4KZ9BVp9j2ho4Mh/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rillet.com/EjmEP4KZ9BVp9j2ho4Mh/getting-started/aura-ai-assistant/audit-trail.md).
# Audit Trail
Audit Trail allows you to see exactly how Aura generated a response or completed an action. You can review the data Aura used, the steps it followed, the logic applied during the process, and the records or reports referenced to produce the final result. This is especially useful for accounting and finance workflows where teams need to validate calculations, review supporting data, or confirm how an AI-generated result was produced before approval.
### Understand When Audit Trail Appears
After Aura completes a response, a **View reasoning** button appears below the response. Audit Trail is available for requests where Aura:
| |
| -------------------------------------- |
| Performs calculations |
| Retrieves financial data |
| Generates analysis |
| Prepares actions or accounting entries |
### Open the Audit Trail
Follow these steps to open the Audit Trail:
1. Go to **Aura AI** from the side panel.
2. Submit a request in the conversation.
3. Click **View reasoning** below the response.
Aura opens an interactive reasoning graph showing how the response was generated.
### Understand the Reasoning Graph
The reasoning graph breaks the response into the individual steps Aura performed while generating the result. Each node represents a sub-task Aura completed, such as retrieving data, validating information, performing calculations, generating summaries, or preparing records.
The graph also shows:
| |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| The order in which steps were executed |
| The logic applied at each step |
| Links to the supporting reports and records used during the process |
| A file (for example, CSV) of the exact data state Aura had at the moment it executed, including the actual values it pulled and not just the query parameters used |
Use this file to verify the exact numbers behind an AI-generated entry, reconcile a discrepancy, or share supporting evidence with auditors. Many nodes include clickable links that let you open the underlying records directly from the reasoning view, making it easier to trace calculations back to their source data.
### Example: Reviewing a Bad Debt Journal Entry
The following example shows how Audit Trail can help verify an AI-generated journal entry. Suppose you ask Aura to prepare a journal entry for bad debt expense. Aura may:
| |
| ------------------------------- |
| Review overdue invoices |
| Evaluate related credit memos |
| Calculate the bad debt amount |
| Create a proposed journal entry |
| Request approval before posting |
After the journal entry is generated, you can click **View reasoning** to review how Aura calculated the amount. The reasoning graph may show steps such as:
| |
| ---------------------------------------- |
| Validating the correct bad debt accounts |
| Retrieving overdue invoices |
| Reviewing total invoice balances |
| Evaluating credit memo history |
| Applying the calculation logic |
| Creating the journal entry draft |
You can click into the related invoices or supporting records directly from the graph to verify the calculation details. This allows teams to quickly confirm how the final amount was produced without manually recreating the analysis.
### Use Audit Trail for Validation
Audit Trail helps teams better understand how Aura arrived at a result. You can use it to:
| |
| -------------------------------------------------------------- |
| Review supporting calculations |
| Validate financial analysis |
| Trace values back to source records |
| Confirm the logic behind AI-generated outputs |
| Better understand how actions or recommendations were produced |
This is especially useful before approving accounting entries or reviewing financial results.
### See Also
To learn more about Audit Trail in **Rillet**, review these articles:
* [**Aura AI Assistant**](/EjmEP4KZ9BVp9j2ho4Mh/getting-started/aura-ai-assistant.md)
* [**Getting Started with Aura Assistant**](/EjmEP4KZ9BVp9j2ho4Mh/getting-started/aura-ai-assistant/getting-started-with-aura-assistant.md)
* [**Actions and Approvals**](/EjmEP4KZ9BVp9j2ho4Mh/getting-started/aura-ai-assistant/actions-and-approvals.md)
* [**Aura AI Prompt Examples**](/EjmEP4KZ9BVp9j2ho4Mh/getting-started/aura-ai-assistant/aura-ai-prompt-examples.md)
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## 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, and the optional `goal` query parameter:
```
GET https://docs.rillet.com/EjmEP4KZ9BVp9j2ho4Mh/getting-started/aura-ai-assistant/audit-trail.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
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.