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Duration 14 hours
Course Outline
Introduction to Mistral’s Conversational AI
- Overview of Mistral’s conversational models
- Understanding capabilities and inherent limitations
- Identifying enterprise use cases for AI assistants
Utilizing Mistral Connectors
- Establishing connections with Google Drive, Docs, and Calendars
- Integrating with various SaaS platforms
- Handling authentication protocols and permission management
Implementing Retrieval-Augmented Generation (RAG)
- Understanding the concept of grounding conversational assistants
- Indexing and organizing enterprise data
- Generating context-aware queries and responses
Crafting User Experiences for Assistants
- Core principles of conversational UX design
- Structuring flows for internal tool usage
- Developing engaging customer-facing chat experiences
Integration Strategies and Deployment
- Embedding assistants seamlessly into product workflows
- Leveraging APIs and SDKs for deployment
- Implementing testing and iterative refinement cycles
Performance Evaluation and Monitoring
- Assessing the quality of AI responses
- Utilizing logging and analytics tools
- Establishing loops for continuous improvement
Case Studies and Industry Best Practices
- Analyzing examples from real-world implementations
- Key lessons from enterprise deployment scenarios
- Exploring future trends in conversational assistants
Summary and Recommended Next Steps
Requirements
- A solid grasp of web applications and API structures
- Practical experience in software integration or full-stack development
- Basic familiarity with conversational AI or chatbot technologies
Target Audience
- Product managers
- Full-stack developers
- Integration engineers
