Generative AI: Use cases, applications, solutions and implementation

The integration of generative AI in healthcare is more than just a technological upgrade—it’s a pivotal transformation in how care is delivered and managed.

Generative AI significantly impacts internal audit practices by enhancing risk evaluation, monitoring compliance, and boosting operational efficiency.

Generative AI improves customer experiences by offering personalized services that boost guest loyalty and satisfaction.

Decision Intelligence (DI) is a data-driven approach to decision-making that leverages advanced analytics, machine learning, and artificial intelligence (AI) to empower organizations with actionable insights.

AI creating AI” through AutoML is transforming the field, expanding beyond human-led processes to make AI development more accessible and efficient.

Structured outputs in large language models (LLMs) refer to the ability of these models to generate responses in specific, predefined formats rather than just free-form text.

Causal AI is a branch of artificial intelligence focused on understanding and determining cause-and-effect relationships rather than merely identifying patterns or correlations.

ReACT and function calling agents represent two distinct but powerful approaches to extending the capabilities of LLMs, each with its own strengths and weaknesses.

Composite AI refers to an advanced AI approach that integrates multiple artificial intelligence (AI) technologies to create a more sophisticated, flexible, and intelligent system.

Advanced RAG builds upon the basic principles of naive RAG by incorporating more sophisticated techniques to enhance retrieval accuracy and contextual relevance.

AI-ready data refers to data that is well-prepared and structured in a manner that maximizes its effectiveness for use in artificial intelligence (AI) applications.

A multi-agent system (MAS) comprises several autonomous agents that work together to achieve specific goals.