Reviewing AI Conversations

Your AI agent handles a lot of conversations on its own, and checking in on those exchanges regularly helps you make sure patients are getting accurate, helpful responses and that nothing important is slipping through the cracks.

Where to Find AI Conversation Logs

Every conversation your AI handles is saved in your inbox just like a message from any other channel. To review them, open the Conversations tab from the main menu. You can filter the list to show only AI-handled threads by using the filter options at the top of the inbox and selecting the AI agent as the assigned contact or conversation type.

Each conversation shows you the full back-and-forth between the patient and the AI, including timestamps and any notes about what action the AI took, such as booking an appointment or handing off to a staff member.

Reading the Conversation Transcript

When you open an AI-handled conversation, you will see a transcript of every message sent and received. The AI responses are labeled so you can tell them apart from anything your staff typed. Look through the exchange the same way you would review a phone call: did the AI understand what the patient was asking? Did it give them the right information? Did it offer to book them in, and if so, did that booking go through?

Pay attention to any point where the conversation stalled, where the patient had to repeat themselves, or where the AI gave a response that seems off. These are the moments worth noting for training purposes.

Spotting Responses That Need Attention

Not every AI conversation needs a deep review. Focus your time on threads where:

• The patient seemed confused or asked the same question more than once
• The AI gave a vague or incomplete answer
• A handoff to your front desk was triggered unexpectedly
• An appointment was not completed even though the patient expressed interest
• The patient never replied after the AI's first response

If you spot a response that was clearly wrong or could have been better, that is a direct signal to update your AI's training. See Training Your AI on Your Practice for how to add or adjust information so the AI handles similar questions better going forward.

Taking Action from the Conversation View

You do not have to leave the conversation to act on what you find. From the same thread, you can:

1. Reply to the patient directly if they need a human follow-up
2. Book or modify an appointment on their behalf using the calendar tools in the right panel
3. Add an internal note to flag something for a colleague without messaging the patient
4. Tag the contact or update their record if you notice missing information

Using Reviews to Improve Your AI Over Time

Reviewing conversations is not just a quality check. It is the main way you improve how the AI represents your practice. Each time you find a gap, that is an opportunity to add a clear answer to your FAQ and knowledge base, refine your AI's tone, or clarify a policy that the AI is describing inconsistently.

A good rhythm is to review a handful of AI conversations at least once a week, especially in the first month after you launch the AI. As you make improvements and responses become more reliable, you can check in less frequently and focus only on edge cases or patient-initiated escalations.

Building a Review Routine

The easiest way to stay on top of AI conversation quality is to build a short weekly review into your team's workflow. Assign one person, often whoever manages the front desk communications, to scan the previous week's AI threads and flag anything worth discussing. Keep a simple running list of questions the AI struggled with so you can address them in one session rather than one at a time.

Over time, you will find that the AI handles more and more conversations cleanly, and reviews become faster as a result.

If you want to go deeper on keeping your AI performing well, read through AI Best Practices and Guardrails for a full checklist of settings, tone guidelines, and safety controls. You may also want to review Business Hours and Handing Off to Staff to make sure your AI knows exactly when to step back and put a human in front of the patient.