Ask in plain language why a rule gave a result
Also listed as: Conversational rule debugging
What is it?
Your team will be able to ask an AI assistant, in plain language, why a rule produced a particular result, and get an explanation based on the rule's trace.
Why does it matter?
Explaining a rule result means reading traces and code, which takes specialist time when staff or constituents query a decision.
Faster answers when a decision is questioned and quicker fixes when a rule is wrong.
Getting it into your solution
- Needs an upgrade to the Xelence release
- Needs configuration
Where people stay in control
The assistant explains and suggests. People decide whether a rule is wrong and make any change.
Start thinking about
- Which rule results do staff or constituents query most often?
- Who should be able to ask the assistant about live decision data?
- What rules apply in your organization to using AI tools with case data?
Terms used on this card
- AI assistant
- A feature that suggests or drafts something, such as a form, report, rule or letter, for a person to review and accept.
- Tracing
- Recording each step a request takes through the system, so support teams can see exactly where something slowed down or failed.
Items shown as planned are subject to change based on customer priorities for specific features and enhancements.