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Deep-Dive into AI Use-Cases

In this module, we will undertake a comprehensive exploration of Project Miyagi and the Reddog codebase. This deep dive will highlight a range of use-cases that can be adopted into your own applications, thereby enabling you to infuse AI to address the diverse needs of your end users.


For detailed implementation of the use-cases, see miyagi/services

Use Cases

  1. Synthesis: Learn how information is synthesized in Miyagi to generate insights, orchestrated by Semantic Kernel.
  2. Multi-modal Generation: Discover how Generative AI is used in Miyagi and Reddog to generate text, images, and videos.
  3. Conversation: Deep-dive into how Miyagi Chatbot is built using Semantic Kernel's Copilot Chat, which you can leverage to build your own Copilot.
  4. Summarization: Understand how to create TL/DRs in your apps using Miyagi's example.
  5. Translation: Language translation.
  6. Code generation: Code generation with SK planner and CoT.
  7. Classification: Explore how Miyagi uses SK to classify expense categories.
  8. Speech-to-Text: Leverage Whisper to perform SoTA speech-to-text, which is used in Miyagi chatbot.
  9. Semantic/Neural Search: Learn how to improve search results with AI's understanding of context.
  10. Anomaly Detection: Discover how AI can identify unusual patterns that could indicate potential issues.
  11. Plugins: Understand how to extend and enhance your applications with ChatGPT and BingChat plugins.
  12. Agency and Planning: Learn how Miyagi uses SK's planner and Jarvis for agentic planning to rebalace portfolio based on user's preferences.