Guiding Users
These docs are for version 1.x of Rasa Open Source.
User Guide
- Installation
- Tutorial: Rasa Basics
- Tutorial: Building Assistants
- Command Line Interface
- Architecture
- Messaging and Voice Channels
- Testing Your Assistant
- Setting up CI/CD
- Validate Data
- Configuring the HTTP API
- Deploying Your Rasa Assistant
- Cloud Storage
NLU
- About
- Using NLU Only
- Training Data Format
- Language Support
- Choosing a Pipeline
- Components
- Entity Extraction
Core
- About
- Stories
- Domains
- Responses
- Actions
- Reminders and External Events
- Policies
- Slots
- Forms
- Retrieval Actions
- Interactive Learning
- Fallback Actions
- Knowledge Base Actions
Conversation Design
API Reference
- Action Server
- HTTP API
- Jupyter Notebooks
- Agent
- Custom NLU Components
- Rasa SDK
- Events
- Tracker
- Tracker Stores
- Event Brokers
- Lock Stores
- Training Data Importers
- Featurization of Conversations
- TensorFlow Configuration
- Migration Guide
- Rasa Open Source Change Log
Migrate from (beta)
Reference
Versions
viewing: 1.10.12
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:
Can I get a large hawaiian and bbq pizza? Sure, that's one large hawaiian and one regular bbq pizza. Anything else? 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.
Example:
I just moved I'm not sure I understood you correctly. Do you mean ...
I want to cancel my contract 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.
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:
What can you do? I can help you update your personal details, change your plan, and answer any questions you have about our products. Can you get me a pizza? I'm afraid I can't help with that.
## 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:
Was that helpful? no.
Thanks. Why wasn't I able to help? you didn't understand me correctly you understood me, but your answers weren't very helpful.
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"]
And make sure 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! There are multiple reasons why you might trigger a human handoff:
- the user asks to speak to a human
- the assistant is struggling to understand the user
- the assistant understands what the user wants, but a human is required to resolve the issue.
Example:
let me speak to a human let me put you in touch with someone.
intents:
- request_human: {"triggers": "action_human_handoff"}