Why a glossary matters
The AI space moves fast, and that speed brings an avalanche of new terms and slang. TechCrunch’s glossary offers a straightforward reference so anyone — engineers, product managers, reporters, or curious readers — can quickly grasp what people mean when they talk about LLMs, hallucinations, or prompt engineering. By translating jargon into plain language, the guide lowers the barrier to understanding and helps more people participate in AI conversations.
Key concepts explained. The glossary covers foundational topics and everyday phrases you’ll encounter in headlines and technical docs. Examples include:
- LLMs (Large Language Models) — what they are and how they differ from other models;
- Hallucinations — why models sometimes generate inaccurate or fabricated outputs and how teams mitigate them;
- Prompt engineering & fine-tuning — practical ways to shape model behavior;
- RLHF, inference, model cards — shorthand for training techniques, runtime behavior, and documentation that improve transparency.
Who benefits and how to use it. This guide is valuable for non-experts trying to make sense of product claims, for startups deciding how to integrate models, and for policy teams assessing risks. Use it as a quick reference when reading AI coverage, drafting requirements, or discussing safety measures with stakeholders.
By making AI vocabulary accessible, the glossary encourages informed adoption and better public discussion — a small but meaningful step toward safer, more productive deployment of AI technologies.