A weekly AI chatbot review that improves real conversations
Review chatbot answers by consequence, repair weak source information and retest handoffs without turning quality checks into a huge project.
A chatbot can become less useful without any software change. The business updates its hours, a promotion ends or customers begin asking about a new product. A weekly review helps the team notice the gap between current operations and automated answers.
This guide is published by the team behind Flas CRM. We sell one of the products in this category, so treat it as a vendor's checklist — the criteria are written to be useful whichever tool you end up choosing.
Choose a sample with variety
Review routine answers, human handoffs, unanswered questions and conversations where the customer repeats themselves. Do not sample only the exchanges that ended neatly. Include a few cases from busy periods and outside working hours if the bot operates then.
Use only the information needed for the review and keep customer details within the team's approved process. The purpose is to improve the answer and workflow, not to build a collection of unnecessary personal data.
Ask four questions about each exchange
- Did the answer address the customer's actual question?
- Were the factual claims supported by current approved information?
- Did the response stay within the bot's intended scope?
- Was the next action or human handoff clear?
These questions make the review concrete. “The tone was fine” is not enough if the bot supplied an expired price or told the customer an unavailable agent would respond immediately.
Prioritise the consequence
| Finding | Response |
|---|---|
| Unsupported commercial promise | Correct the source or scope promptly and review affected handling |
| Failed handoff | Check both the trigger and the receiving team's ownership |
| Repeated missing answer | Add an approved answer or a clear escalation path |
| Awkward but accurate wording | Improve the phrasing after material issues are addressed |
Fix the smallest responsible part
If the opening hours are wrong, update the source. If the bot answers a question that should go to sales, adjust the scope and handoff. If the transfer arrives but nobody responds, fix team ownership. Avoid treating every failure as a prompt-writing problem.
Flas offers the AI chatbot, handoff and monitoring capabilities to evaluate in this workflow. Check which records and controls your configuration provides. This review method does not assume a dedicated automatic quality-scoring feature.
Retest and keep a short change record
Repeat the failed question and a few related variations after making the correction. Note what changed, why and who checked it. This helps the next reviewer understand whether a later problem is new or a return of an earlier issue.
Use the test plan for repeatable cases and the knowledge checklist for source ownership. A useful weekly review produces a small number of verified improvements, not a large report that nobody acts on.
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