Designing Conversational Flows

Learning Objectives

• Design effective linear and non-linear conversation flows
• Implement context management across conversation turns
• Create conditional logic in conversational interfaces
• Design effective error recovery strategies
• Map user journeys through conversational experiences

Dialogue Management Basics

Dialogue management is the heart of any conversational interface. It's the system that controls the flow of conversation, maintains context, and determines appropriate responses based on user inputs and the current state of the interaction.

At its core, dialogue management treats conversation as a state machine, where each state represents a particular point in the conversation, and transitions between states occur based on user inputs and system logic.

Key Concepts in Dialogue Management

Turn-taking

The back-and-forth exchange between user and system, with clear indicators of who has the floor at any given moment.

Conversation States

Distinct points in a conversation that represent the current context and determine what happens next.

State Transitions

Rules that determine how the conversation moves from one state to another based on user inputs and system logic.

Context Management

The ability to maintain relevant information across multiple turns of a conversation.

Linear vs. Non-linear Dialogue Structures

Conversational interfaces can follow different structural patterns, each with its own advantages and appropriate use cases.

Linear Dialogue Flows

Linear dialogues follow a predetermined sequence of steps, guiding users through a specific process with limited deviation. Think of them as conversational forms or wizards.

Advantages:

• Predictable and easy to design
• Ensures all necessary information is collected
• Provides clear guidance to users
• Simpler to implement and test

Best for:

• Form-filling tasks (e.g., booking appointments)
• Step-by-step processes (e.g., troubleshooting)
• Onboarding sequences
• Guided tutorials

Non-linear Dialogue Flows

Non-linear dialogues allow users to drive the conversation, jumping between topics and taking different paths based on their needs. They're more flexible but also more complex to design and implement.

Advantages:

• More natural conversational experience
• Accommodates diverse user needs and paths
• Allows users to control the interaction
• Can handle unexpected inputs more gracefully

Context Management

One of the most challenging aspects of designing conversational interfaces is maintaining context across multiple turns of a conversation. Without proper context management, conversations feel disjointed and users have to repeatedly provide the same information.

Types of Context

Conversational interfaces need to track several types of context:

• Dialogue context: Where we are in the current conversation flow
• Entity context: Information provided by the user (e.g., dates, locations, preferences)
• User context: User-specific information (e.g., preferences, history, account details)
• System context: The state of backend systems and processes

Session Attributes in Amazon Lex

In Amazon Lex, context is primarily managed through session attributes. These are key-value pairs that persist throughout a user session and can be accessed and modified by both the Lex service and your Lambda functions.

// Example Lambda function showing context management
exports.handler = async (event) => {
    // Extract session attributes or initialize if none exist
    const sessionAttributes = event.sessionAttributes || {};
    
    // Get the current intent
    const intentName = event.currentIntent.name;
    
    // Handle different intents
    if (intentName === 'BookFlight') {
        // Store flight details in session attributes
        sessionAttributes.departureCity = event.currentIntent.slots.DepartureCity;
        sessionAttributes.destinationCity = event.currentIntent.slots.DestinationCity;
        sessionAttributes.travelDate = event.currentIntent.slots.TravelDate;
        
        return {
            sessionAttributes: sessionAttributes,
            dialogAction: {
                type: 'Close',
                fulfillmentState: 'Fulfilled',
                message: {
                    contentType: 'PlainText',
                    content: `I've booked your flight from ${sessionAttributes.departureCity} to ${sessionAttributes.destinationCity} on ${sessionAttributes.travelDate}.`
                }
            }
        };
    }
};

Implementing Conditional Logic

Effective conversational interfaces need to adapt their behavior based on user inputs, context, and external factors. This is where conditional logic comes in.

Decision Points in Conversations

Decision points are moments in a conversation where the flow can branch in different directions based on certain conditions. These might include:

• User responses to specific questions
• Presence or absence of certain information
• Results from external API calls or database queries
• User preferences or history
• Time-based conditions

Conditional Flow Builder

Start: Greeting

Decision: User has account?

Yes

Login Flow

No

Registration Flow

Designing for Error Recovery

No conversational interface is perfect. Users will say unexpected things, misunderstand prompts, or change their minds mid-conversation. Effective error recovery strategies are essential for creating resilient conversational experiences.

Types of Conversational Errors

Several types of errors can occur in conversational interfaces:

• No Match Errors: The system doesn't understand the user's input
• No Input Errors: The user doesn't respond within the expected timeframe
• Out-of-Domain Errors: The user asks for something outside the system's capabilities
• Slot Filling Errors: The system can't extract required information from the user's input
• Context Errors: The system loses track of the conversation context

Graceful Error Handling

Effective error handling strategies include:

1. Clear Error Messages: Explain what went wrong in simple terms
2. Helpful Guidance: Suggest what the user can do next
3. Escalation Paths: Provide ways to get human help when needed
4. Context Preservation: Don't lose progress when errors occur
5. Progressive Assistance: Offer more help with repeated failures