Guiding Users

Guiding Users

Implicit Confirmation

Implicit confirmation involves repeating details back to the user to reassure them that they were understood correctly. This also gives the user a chance to intervene if your assistant misunderstood.

Example:
User: Can I get a large hawaiian and bbq pizza
Assistant: Sure, that's one large hawaiian and one regular bbq pizza.

User: Anything else?
Assistant: No they should both be large!

Explicit Confirmation

Explicit confirmation means asking the user to clarify how you should help them. An important thing to remember about AI assistants is that the user is never wrong. When a user tells you something like I just moved, they are being perfectly clear, even if your assistant is not sure how to help them.

Example:
User: I just moved
Assistant: I'm not sure I understood you correctly. Do you mean ...
User: I want to cancel my contract
Assistant: I want to update my personal details

You can configure the TwoStageFallbackPolicy to ask your user to clarify, and present them with quick replies for the most likely intents. To do this, configure the policy as in this example:

policies:
- name: TwoStageFallbackPolicy
  nlu_threshold: 0.3
  core_threshold: 0.3
  fallback_core_action_name: "action_default_fallback"
  fallback_nlu_action_name: "action_default_fallback"
  deny_suggestion_intent_name: "out_of_scope"
...

Explaining Possibilities

AI assistants are always limited to helping users with a specific set of tasks, and should be able to explain to a user what they can do. That includes coherently responding to requests that are out of scope.

Example:
User: What can you do?
Assistant: I can help you update your personal details, change your plan, and answer any questions you have about our products.

User: Can you get me a pizza?
Assistant: I'm afraid I can't help with that.

To gracefully handle out-of-scope requests, you might use stories such as:

## user asks whats possible
* ask_whatspossible
  - utter_explain_whatspossible

## user asks for something out of scope
* out_of_scope
  - utter_cannot_help
  - utter_explain_whatspossible

Collecting User Feedback

Asking for feedback is one of the best tools you have to understand your users and determine whether you solved their problem! Storing this feedback is a powerful way to figure out how you can improve your assistant.

Example:
User: Was that helpful?
Assistant: no.

User: Thanks. Why wasnt I able to help?
Assistant: you didn't understand me correctly
Assistant: you understood me, but your answers weren't very helpful.

To collect feedback, define a custom form action:

from rasa_sdk.action import FormAction

class FeedbackForm(FormAction):

def name(self):
        return "feedback_form"

@staticmethod
    def required_slots(tracker):
        return ["feedback", "negative_feedback_reason"]

Add the form and slots to your domain:

forms:
  - feedback_form
slots:
  feedback:
    type: bool
  feedback_reason:
    type: text
  requested_slot:
    type: text

And ensure the FormPolicy is present in your configuration file:

policies:
  - FormPolicy
  ...

Handing off to a Human

Users will be very frustrated if your assistant cannot help them and there is no way to reroute the conversation to a human agent. There should always be a way to break out of a conversation!

Example:
User: let me speak to a human
Assistant: let me put you in touch with someone.

There are various reasons why you might trigger a human handoff:

For instance, you can handle a direct request with the mapping policy:

intents:
  - request_human: {"triggers": "action_human_handoff"}