This category covers generative AI models and retrieval-augmented generation, which connects model outputs to relevant external information. These approaches shape how organizations build AI systems that can respond to specialized questions with grounded, current context.
Agentic AI frameworks are software tools for building, coordinating, and managing AI agents that can take actions toward goals.
AI edge processors are hardware chips or processing units designed to run artificial intelligence workloads on or near the devices that generate data.
Generative AI models are artificial intelligence systems trained to produce new content, such as text, images, audio, video, or code.
Neuromorphic AI chips are processors designed to perform computation using principles inspired by the structure or activity of biological neural systems.
Retrieval-augmented generation is an approach that supplies a generative language model with relevant information retrieved from external sources when producing a response.
Small language models are language models designed with fewer parameters or lower computational requirements than larger models.
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