Join us for an educational 3-hour workshop where you will explore applying Retrieval Augmented Generation (RAG) to create a chatbot application with private data using Elastic. You will also learn to deploy a publicly available Large Language Model (LLM) on Amazon SageMaker, utilize Elasticsearch's hybrid search, and integrate private data for improved chatbot performance. 

The workshop involves integrating Elastic, a robust search engine, with an existing LLM on Amazon SageMaker and Amazon Bedrock to address domain-specific questions.

• Utilizing Elasticsearch:
  -Run hybrid semantic search queries to retrieve relevant private data from Elastic.
  -Leverage BM25 for textual search and KNN for semantic understanding.
  
  
• Contextualizing LLMs:
  -Modify the chatbot to feed fetched data from Elastic as context to the LLM.
  - Observe how enriched context improves LLM responses for domain-specific questions.
  
  
• LLM Deployment:
  -Understand choosing and deploying the best LLM for their use case within Amazon SageMaker or Bedrock
  
  
• Customer Applications:
  -Improved Chatbot application Accuracy: Elevate chatbot performance for domain-specific inquiries by leveraging their private data in Elastic.
  -Enhanced Customer Experience: Offer more relevant and accurate information, leading to increased customer satisfaction.
  -Personalization Opportunities: Tailor chatbot responses to individual user needs by incorporating their past interactions and preferences.
  -Efficient Workflow Automation: Reduce manual query resolution by empowering the chatbot to handle complex questions.
  

12:00 - Check In / Lunch

12:45 - 1:45   Presentation

1:45 - 2:30    Workshop
2:30 - 3:00   Q&A