Leveraging Data as Foundation for Generative AI Applications: Travel Agent, Chatbot, and Marketing Use Cases

Unlocking the power of data as your differentiator in generative AI applications, from travel agents to contextual chatbots, and discover how Amazon Bedrock can help you build, optimize, and evaluate your innovative solutions.

  • 1. Topic: Data as a differentiator in generative AI applications.
  • 2. The foundation for building large generative AI applications is data.
  • 3. Data requirements for building generative AI applications are different from other types of AI.
  • 4. Special treatment must be given to data based on the specific application and business requirements.
  • 5. Data interaction with technology and people, as well as eliminating data silos, are critical considerations when building generative AI applications.
  • 6. Applications that require large amounts of data include travel agents, conversational chatbots, and marketing tools.
  • 7. A prompt system is necessary for a travel agent application to understand user queries and provide relevant responses.
  • 8. Context in generative AI applications is dynamic and comes from various data services and sources.
  • 9. Responsible AI practices must be considered when building generative AI applications.
  • 10. Amazon Bedrock provides tools for building custom data pipelines, fine-tuning models, evaluating models, and ensuring responsible AI practices.
  • 11. Generative AI applications can be built quickly using pre-built knowledge bases and reducing time to market.
  • 12. Data processing is critical in generative AI applications, and choosing the right chunking strategy can impact accuracy.
  • 13. Optimization of generative AI applications involves techniques like query reformulation, decomposition, caching, and observability.
  • 14. Semantic caching is a technique to quickly retrieve similar questions and responses without invoking the foundation model.
  • 15. Observability is essential for monitoring and improving generative AI applications by logging user queries, retrieval hits, and model responses.
  • 16. Evaluation of search results and other metrics is necessary before augmenting prompts and giving them to the model.
  • 17. Updating data and strategies based on evaluation is crucial in optimizing generative AI applications.
  • 18. Testing must be automatic and defined to reduce errors and increase the quality of production applications.
  • 19. Coconuts are used as a metaphor for achieving success in building and deploying generative AI applications.
  • 20. The presentation emphasizes the importance of data, responsible AI practices, optimization techniques, evaluation, testing, and successful deployment.

Source: AI Engineer via YouTube

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