Cortex Cloud AI Security concepts - Basic concepts of Cortex Cloud AI Security. - Administrator Guide - Cortex XSIAM - Cortex - Security Operations

The model is the core component of the AI ecosystem. It is the trained machine learning model that takes input data, processes it, and produces output. In the context of large language models (LLMs), this involves understanding and generating human-like text based on the given input.

The model endpoint is the interface through which applications interact with the AI model. It acts as an access point for sending inputs to the model and receiving outputs. The endpoint is responsible for managing requests, routing them to the appropriate model instance, and returning the results to the application.

A plugin is an auxiliary but highly capable model or tool that acts as a helper to the primary AI model. Plugins extend the functionality of the main model by providing specialized capabilities, such as accessing inference datasets, performing specific computations, or interfacing with other services. This approach, known as retrieval-augmented generation (RAG), enhances the primary model's ability to generate more accurate and contextually relevant outputs. For more information, see Inference datasets and Retrieval-Augmented Generation.

Training is a fundamental stage in the AI development process where the model learns to perform its tasks by processing large amounts of data. During this phase, the model is exposed to various examples and adjusts its internal parameters to minimize errors in predictions or classifications. The dataset is the integral part of the process, with the insights learned by the model influenced by the training data.

Inference datasets are specialized collections of data used during the inference phase of AI models, which is the stage where the model makes predictions or generates outputs based on new input data. Unlike training datasets, which are used to teach the model how to understand and process information, inference datasets help improve the model's performance by providing realistic, real-world data inputs for better contextual answering.

Fine-tuning in machine learning refers to the process of adapting a pre-trained model to perform specific tasks or cater to particular use cases. This technique has become essential in deep learning, especially for training foundation models used in generative AI. Fine-tuning leverages data (similarly to training) to adjust the responses of the model to certain inputs, making it more suitable for the intended business case.

Retrieval-Augmented Generation (RAG) enhances large language model (LLM) responses by incorporating information from knowledge bases and other sources. This allows the model to reference up-to-date inference data before generating a response, improving contextual accuracy. This approach is cost-effective and ensures the output remains relevant, accurate, and useful across different contexts.

To illustrate how these components work together, consider an AI-powered customer support chatbot: