Why this glossary matters
TechCrunch's AI glossary offers plain-language definitions of the most common terms you’ll encounter when reading about or using AI. As AI moves into products, workplaces, and everyday life, a concise reference helps people spot hype, ask better questions, and make smarter choices about tools and policies.
What you’ll get
The guide covers foundational concepts like models, training, fine-tuning, and inference, plus practical topics such as hallucinations, prompting, and bias. Each entry is written to be accessible to non-experts so readers can quickly grasp how these ideas affect real-world applications.
Practical benefits
With this glossary, developers can communicate more clearly with stakeholders, managers can make better procurement and risk decisions, and everyday users can assess when a tool is appropriate for a task. The result is smarter adoption of AI and fewer misunderstandings.
Quick reference
- Hallucinations: When a model generates false or fabricated information — important to know when verifying outputs.
- Fine-tuning: Adapting a pretrained model to perform better on a specific task or dataset.
- Inference: The act of running a model to produce an output from input data.
Overall, the glossary is a timely, practical resource that lowers the barrier to AI literacy and encourages safer, more effective use of these powerful technologies.