New Plain-English Glossary Helps Workers Navigate AI at Work

A new online resource has been published with the aim of helping people understand artificial intelligence in the workplace. The glossary, produced by United Ceres Workforce Learning, defines 44 common AI terms in plain English. It is designed for workers who may encounter AI systems but lack a technical background in the field.

The glossary covers a wide range of terms, including large language models, prompts, RAG, MCP, AI governance and the PDPA. Terms are listed in alphabetical order, and each term has a fixed link so that other guides can point directly to its definition. The page also groups recommended terms for everyday users, builders, leaders and governance owners. Definitions of Singapore frameworks and laws are based on official PDPC and AI Verify Foundation pages.

Why Clear Definitions Matter

As AI tools become more common across various industries, many employees find themselves needing to understand concepts they had not previously encountered. Terms such as "large language model" or "retrieval-augmented generation" can be confusing when first encountered in a work context. A glossary that explains these terms in everyday language can reduce that confusion and help people participate in discussions about AI projects or policies.

For organisations, having a shared vocabulary around AI is useful. When team members understand the same terms in the same way, it becomes easier to plan projects, evaluate risks and comply with regulations. This is where the concept of AI governance Singapore becomes relevant. The glossary includes a definition of AI governance that is aligned with local frameworks, making it a practical reference for workers and managers who need to apply these concepts in their daily roles.

Many glossaries aimed at technical audiences assume a level of prior knowledge. This resource deliberately avoids jargon in its definitions, focusing instead on explanations that a general reader can follow. That approach can be especially valuable in a workplace setting where people from different departments may need to collaborate on AI-related tasks.

How the Glossary Is Organised

The glossary is structured to make it easy to find a specific term quickly. Terms are listed in alphabetical order, so a user who knows the name of a concept can scroll to the right letter. Each term has its own fixed link, which means other training materials, internal wikis or policy documents can link directly to the definition. This feature helps keep references consistent across an organisation.

Beyond the alphabetical listing, the page groups terms by audience. Everyday users will find a set of basic terms that cover what AI is and how it appears in common tools. Builders get terms related to developing or customising AI systems. Leaders receive terms that help with strategic decision-making. Governance owners are directed to definitions that relate to compliance, ethics and oversight. This grouping saves time for readers who only need the terms relevant to their role.

The inclusion of the PDPA in the glossary is notable. Singapore's Personal Data Protection Act has specific requirements that apply when AI systems process personal data. A clear definition of the PDPA, written in plain English, can help workers understand their obligations without needing to consult a lawyer for every question. Similarly, the term AI governance Singapore is defined in a way that ties together the technical and regulatory aspects of managing AI in a local context.

Background on AI Governance at Work

AI governance refers to the policies, processes and controls that an organisation puts in place to ensure its use of AI is responsible, transparent and compliant with laws. As AI systems become more capable, the need for clear governance frameworks grows. Workers at all levels may need to understand what governance means in practice, from how data is collected to how decisions made by AI are reviewed.

In Singapore, the government has published frameworks such as the Model AI Governance Framework and the AI Verify testing toolkit. These resources help organisations assess their AI systems against principles like fairness, accountability and explainability. A glossary that references these official sources can serve as a bridge between high-level policy and day-to-day work.

For many employees, the first encounter with AI governance comes when they are asked to fill out a data sheet or attend a training session. Having a simple definition to refer to can make those tasks less intimidating. It also helps people identify when a question or concern should be escalated to a governance owner.

The rise of large language models and generative AI tools has made governance more urgent. These tools can produce text, images and code that look human-made, which raises questions about accuracy, bias and intellectual property. Organisations that deploy such tools need clear rules about what is allowed and how outputs should be verified. A shared vocabulary is a foundational step in building that discipline.

How This Resource Fits Into Workplace Learning

Workplace learning about AI does not have to start with a technical course. Often, the most effective first step is a glossary that explains the key terms in language that anyone can understand. Once people know what terms like "prompt" or "RAG" mean, they can follow more detailed training with greater confidence.

The glossary also supports self-directed learning. A worker who encounters an unfamiliar term in a meeting or a document can look it up on the spot, without waiting for a formal training session. The fixed links mean that a manager can include a term in an email and link directly to its definition, saving time and reducing misunderstandings.

For organisations that are developing their own AI policies, the glossary can serve as a reference that every employee can access. When policies use terms that are defined in the glossary, there is less room for misinterpretation. This alignment can be especially helpful during audits or regulatory reviews, where consistent understanding of terms matters.

Looking Ahead

As AI continues to evolve, the terms used to describe it will likely change as well. New tools and techniques will bring new vocabulary. A glossary that is maintained and updated can remain a useful resource over time. The inclusion of fixed links makes it possible for other materials to reference specific definitions without breaking when the glossary expands.

For now, the resource provides a practical starting point for anyone who wants to make sense of AI at work. By focusing on plain English and drawing on official Singapore frameworks, it addresses a real need in the local workforce. Workers who understand the basics of AI are better equipped to use it safely and effectively, and to ask the right questions when something is unclear.