// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
AI Policy
Guidelines and strategies, often from governments or organizations, that shape the development and use of AI.
TECHNICAL DEFINITION
AI Policy comprises the strategic guidelines, principles, and recommendations formulated by governments, organizations, or international bodies to direct the research, development, deployment, and societal integration of artificial intelligence technologies.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt and prompt contexts supplied to the GenAI model, such as system instructions, metadata, API tools and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- AI strategy
- AI guidelines
- AI framework
- AI directives
USAGE NOTE
National AI policy often outlines priorities for investment and ethical considerations.
DEVELOPERS
Organizations developing technology related to AI Policy.
Provides an AI governance platform that helps organizations operationalize responsible AI by managing risk, compliance, and auditing AI systems against internal policies and external regulations.
Develops a Model Performance Management (MPM) platform that provides explainability, monitoring, and fairness analysis for AI models, enabling companies to validate, manage, and comply with AI policies in production.
Offers an AI performance platform that monitors, measures, and improves machine learning models for accuracy, explainability, and fairness, helping organizations enforce their AI policies and mitigate risks.
Provides tools and platforms like Watsonx.governance to help enterprises automate and manage the AI lifecycle for risk and compliance, enabling policy enforcement through model tracking, bias detection, and explainability.
Develops Responsible AI tools within its Azure AI platform, including a Responsible AI Dashboard for debugging models, assessing fairness, and ensuring compliance with organizational and regulatory policies.
Creates an AI Quality platform for diagnostics, testing, and monitoring of machine learning models. The technology helps organizations ensure model performance, explainability, and fairness in alignment with AI policies.
An enterprise AI platform that integrates governance and guardrails throughout the MLOps lifecycle, providing tools for compliance documentation, bias and fairness testing, and risk management to enforce AI policies.
A non-profit organization that develops tools, frameworks, and best practices for responsible AI. It creates resources like the Responsible AI Licensing (RAIL) Initiative to help operationalize ethical AI policies.