Generative AI: Use cases, applications, solutions and implementation

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.

Cloud AI services are cloud-based platforms and tools that provide users with prebuilt AI capabilities, infrastructure, and development environments.

Generative AI empowers HR professionals to concentrate on more strategic issues by automating routine tasks and offering data-driven insights.

AI TRiSM helps understand and mitigate the risks associated with AI, ensuring compliance with evolving regulations and maintaining the integrity and security of AI systems.

AI agents offer a multitude of benefits for legal document management, transforming how legal professionals handle tasks such as document review, drafting, compliance checks, and legal research.