1. Conversational AI DesignIndustry Use Cases: Identifying and applying AI for customer service, technical support, and digital assistant scenarios.Best Practices: Implementing principles of conversational design and user experience.Flow Design: Creating logical conversational paths, handling complex user intents, and managing context.Handoff Strategy: Designing seamless transitions from virtual assistants to live human agents. IBMDomain & Content: Defining scope, channel-specific content, and integration requirements.2. Build Conversational FlowsActions & Responses: Building robust actions, managing user turns, and crafting effective system responses.AI Configuration: Utilizing built-in AI capabilities to enhance assistant understanding.Testing & Debugging: Implementing verification processes to ensure flow accuracy and troubleshooting common conversation errors.3. Build Back-End IntegrationsOpenAPI Extensions: Connecting the assistant to external services and systems.Webhooks: Implementing and managing webhooks for real-time data exchange and dynamic responses. IBM4. Integrate with watsonxGenerative AI Capabilities: Leveraging incorporate LLM power into conversational experiences.Retrieval-Augmented Generation (RAG): Building conversational search patterns to ground AI responses in enterprise knowledge bases. IBM5. Multi-modal IntegrationCross-Channel Management: Managing the assistant presence across different platforms.Channel Integration: Connecting to external interfaces incl
What you'll learn
design conversational flow paths
implement AI configuration strategies
integrate back-end services using OpenAPI
utilize retrieval-augmented generation techniques
manage cross-channel assistant presence
Course objectives
master conversational AI design
build and debug effective conversational flows
create integrations with external systems
leverage the latest AI technologies for enhanced user experiences