# Definitions + **Agent:** An AI system that can perform tasks autonomously and interact with its environment to achieve specific goals. + **Bias and fairness testing:** Evaluating and mitigating potential biases or unfair outcomes from AI models, particularly in areas like gender, race, or age. + **Continuous pre-training:** The process of continuously updating a pre-trained model with new data to improve its performance and adapt to evolving domains or tasks. + **Chunking:** Breaking up large data files into small, discreet chunks to allow the foundation model to fit that data into a context window. + **Data management:** The process of identifying, collecting, storing, aggregating, searching, tracking, governing, and using data. + **Embedding:** Transforms chunks of data into vectors that represent semantic meaning. + **Fine-tuning:** The process of adapting a pre-trained model to a specific task or domain by training it on a smaller, task-specific dataset. + **Foundation models:** Large language models pre-trained on vast amounts of data, serving as a foundation for downstream tasks and fine-tuning. + **Foundation model providers:** Companies or organizations that develop and release foundation models for use by others. + **Generative AI:** AI systems capable of generating new content, such as text, images, or code, based on input data or prompts. + **Hallucination:** A phenomenon where a generative AI model produces outputs that are inconsistent, factually incorrect, or unrelated to the input prompt. + **Human oversight:** Mechanisms for human experts to review, validate, and control critical decisions or outputs from AI models. + **Indexing:** Process of inserting embedded chunks into a vector data store. + **Knowledge graph:** A structured representation of real-world entities and their relationships, used to enhance the contextual understanding and reasoning capabilities of AI systems. + **LLMOps or GenAIOps:** Operational practices and principles for managing the lifecycle of large language models (LLMs), including model selection, data preparation, deployment, monitoring, and governance. + **Model card:** A document that provides key information about a machine learning model, including its intended use, training data, performance characteristics, and potential limitations or biases. + **Model customization:** The process of modifying a foundation model using various techniques to control its behavior. + **Model distillation:** A technique for creating a smaller, more efficient model that mimics the behavior of a larger, more advanced model. + **Model evaluation:** The process of assessing the performance, robustness, and other characteristics of language models using various metrics and techniques. + **Model gateway:** An interaction layer offering secure access to the model hub through standardized APIs. + **Model hub:** A central repository providing access to enterprise foundation models from first-party, third-party, and open-source providers. + **Model interpretability:** The ability to understand and explain the reasoning behind a model's outputs, increasing transparency and interpretability. + **Model orchestration:** Encapsulation of multistep workflows which are characteristic of generative AI workflows. + **Pre-Training:** Building a foundation model from scratch. Requires GPU clusters to run continuously for weeks. + **Prompt catalog:** A centralized repository for storing, managing, and versioning prompts used to interact with generative AI models. + **Prompt engineering:** The practice of carefully crafting prompts to guide language models to produce desired outputs. + **Provisioned throughput:** Feature of Amazon Bedrock that allows you to provision a higher level of throughput at a fixed cost for predictable, high-throughput workloads. + **Quantization:** Techniques for reducing the precision of model parameters, thereby decreasing the memory footprint and computational requirements. + **Responsible AI:** The practice of developing and deploying AI systems in a manner that prioritizes fairness, transparency, accountability, and adherence to ethical principles. + **Retrieval-Augmented Generation (RAG):** A technique/architectural style where a language model's output is augmented with relevant information retrieved from a corpus of documents. This technique is employed to make sure the responses are grounded with the documents and to reduce hallucination. + **Self-hosted models:** AI models that are deployed and managed by the organization using them, rather than relying on a third-party provider. + **Serverless architecture:** An architecture pattern where the cloud provider automatically manages the allocation and provisioning of computational resources, allowing for scalability and cost optimization. + **Tokenization:** The process of breaking down input text into smaller units called tokens, which can be words, subwords, or characters, as a preprocessing step for natural language processing tasks. + **Vector store:** A specialized data store for efficient storage and retrieval of high-dimensional vector embeddings, often used in semantic search and retrieval tasks. Vector stores such as Amazon OpenSearch Service serverless support different search algorithms. + **Zero-shot learning:** The ability of a model to perform a task or make predictions on examples it has never seen before, without requiring task-specific training data. For the latest AWS terminology, see the [AWS glossary](https://docs.aws.amazon.com/glossary/latest/reference/glos-chap.html) in the AWS Glossary Reference.