AI is rewriting the world with a new language of its own
This glossary demystifies the jargon that dominates product meetings and panels. It covers terms like LLMs and RAG, which are reshaping how AI is built and used. The guide is designed for anyone navigating the fast-evolving AI landscape.
Artificial intelligence is rewriting the world, and simultaneously inventing a whole new language to describe how it’s doing it. Sit in on any product meeting, pitch, or panel these days, and you’ll hear people toss around LLMs, RAG, RLHF, and a dozen other terms that can make even very smart people in the tech world feel a little insecure. This glossary is our attempt to fix that: pain-English definitions of the AI terms you’re most likely to actually run into, whether you’re building with this technology or simply trying to understand it.
The field of AI is evolving rapidly, and with it comes a growing list of acronyms and technical terms that can be confusing for even seasoned professionals. Terms like large language models (LLMs), retrieval-augmented generation (RAG), and reinforcement learning with human feedback (RLHF) are now part of the everyday conversation in tech circles. Understanding these terms is essential for anyone looking to engage with AI development, deployment, or regulation.
Large language models, or LLMs, are the AI models used by popular AI assistants, such as ChatGPT. These models are trained on vast amounts of text data and are capable of generating human-like responses to a wide range of queries. As of 2024, the landscape of AI is more complex than ever, with new terms and concepts emerging at a rapid pace. Experts in the field are still working to define and standardize many of these terms, as seen in discussions from 2024.
The proliferation of AI terminology has created challenges for both developers and end-users. Companies and organizations must navigate a landscape where definitions can vary widely, and the same term may mean different things in different contexts. This lack of standardization can lead to confusion, inefficiencies, and even misaligned expectations between stakeholders. As the field continues to grow, the need for clear, consistent definitions becomes increasingly important.
The glossary serves as a critical resource for anyone trying to make sense of the AI landscape. By providing clear, concise definitions of key terms, it helps bridge the gap between technical experts and the broader audience. As AI becomes more integrated into various industries, having a shared understanding of terminology will be essential for collaboration, innovation, and effective governance.