For customers who are starting fresh with AI on AWS, we will delve into topics such as:

• The impact of Generative AI, specifically in securely handling vast data pools and providing up-to-date, accurate information
• Key concerns, like how to reduce Generative AI hallucinations and ensure data is secure
• Setting up an AI-based chatbot, like Clementine, that uses third party data sources.
• Leveraging Clementine's architecture to build a bespoke chatbot tailored to your specific documentation.

For those looking to improve their current Generative AI operations, we will provide guidance on:

• Enhancing the accuracy of your AI system's outputs and improving data privacy.
• Reducing hallucinations and providing more context in your AI responses.
• Utilizing AWS services, such as Lambda and SageMaker, to augment your AI capabilities.
• Enriching user experience by integrating direct links to relevant documentation within AI responses.

Potential use cases for chatbots and specialized search Life Sciences

• hatbot: Help researchers retrieve specific study methodologies from a knowledge base
• Search: Expedite drug discovery by finding relevant internal documentation, clinical trials and literature research Healthcare
• Chatbot: Facilitate patient interactions for quick answers on medical procedures
• Search: Enhance patient care by finding similar symptoms and diagnoses in medical records
  Software
• Chatbot: Assist users with software installation and resolve technical issues
• Search: Improve debugging by locating related bugs and their solutions in community forums Fintech
• Chatbot: Assist customers with real-time account balance inquires
• Search: Improve risk assessment by identifying similar financial scenarios and their outcomes.