AI researcher Oren Etzioni is challenging the technology industry to explain its language more clearly, translating about 30 common terms into plain English.
His guide addresses a growing communication problem. Terms such as LLM, open weights, RAG, and agent now appear in product announcements, investor presentations, and public debates. Yet many people using them cannot define them precisely.
Etzioni pairs each explanation with a direct assessment. He also points to one phrase that he believes deserves wider use, inviting readers to identify it among the entries.
AI Vocabulary Spreads Faster Than Understanding
Artificial intelligence has moved from research labs into workplaces, schools, government agencies, and consumer products. Its specialist vocabulary has followed.
The speed of adoption can blur important distinctions. A familiar term may carry different meanings across research papers, marketing materials, and everyday conversations.
“Everyone in tech says things like LLM, open weights, RAG and agent; far fewer could define precisely what they all mean.”
That gap matters because technical labels often shape decisions about cost, security, copyright, privacy, and reliability. If buyers and policymakers misunderstand a term, they may also misunderstand a system’s limits.
Four Terms at the Center of Debate
The examples selected for Etzioni’s guide reflect several major questions facing the AI sector:
- LLM usually means large language model, a system trained to predict and generate language.
- Open weights generally means a model’s learned numerical parameters are available, although licenses and access conditions can vary.
- RAG refers to retrieval-augmented generation, which supplies a model with selected information before it produces an answer.
- Agent often describes software that can plan or take actions, but the term has no single standard meaning.
These distinctions can affect how users judge a product. Open weights do not always mean fully open-source software. RAG can provide current or specialized material, but it does not guarantee an accurate answer.
Likewise, calling a system an agent may suggest more independence than it has. Some agents complete several steps with limited supervision. Others are standard applications presented with a newer label.
Plain Language Tests Industry Claims
Etzioni’s blunt verdicts add an editorial test to the definitions. Explaining what a term means is only the first step. Readers also need to know whether the idea is useful, overstated, misunderstood, or applied too loosely.
This approach can help separate engineering concepts from sales language. It also gives non-specialists a clearer basis for comparing products that may use the same label for very different capabilities.
Still, simplified definitions have limits. AI terminology changes as researchers publish new methods and companies adjust their products. Meanings can also depend on context, so a short glossary cannot replace detailed technical documentation.
A Call for More Precise Discussion
The unidentified phrase that Etzioni believes is underused adds a small challenge to the guide. More importantly, it signals that the industry may need better language, not merely explanations of existing jargon.
Clear definitions will become more important as AI systems take on larger roles in business and public life. Companies will face pressure to describe what their products actually do, rather than rely on fashionable labels.
Etzioni’s central point is practical: people cannot evaluate AI claims without a shared vocabulary. A plain-English guide cannot settle every dispute, but it can make those disputes more informed. Readers should watch whether the industry adopts tighter definitions, especially for contested terms such as open weights and agent.
Senior Software Engineer with a passion for building practical, user-centric applications. He specializes in full-stack development with a strong focus on crafting elegant, performant interfaces and scalable backend solutions. With experience leading teams and delivering robust, end-to-end products, he thrives on solving complex problems through clean and efficient code.
