From LLMs to hallucinations, here’s a simple guide to common AI terms

TechCrunch has published a practical glossary breaking down the most commonly used artificial intelligence terminology, from foundational concepts like large language models (LLMs) to more nuanced phenomena like hallucinations. The guide is aimed at general audiences trying to make sense of an industry that has developed its own dense vocabulary at a rapid pace.

The Details

The glossary covers a broad range of terms that have become standard in AI conversations but are frequently misunderstood or misused in business contexts.

Key concepts addressed include:

• LLMs (Large Language Models): The underlying technology powering most modern AI chat and voice tools, trained on massive datasets to predict and generate text
• Hallucinations: Instances where an AI model generates confident but factually incorrect or fabricated outputs
• Inference: The process of running a trained model to produce responses, as opposed to the training phase itself
• Prompt engineering: The practice of crafting inputs to AI systems to get more accurate or useful outputs
• Fine-tuning: Adapting a pre-trained model on a smaller, specialized dataset to improve performance in a specific domain

The glossary reflects how quickly AI terminology has moved from research papers into everyday business conversations, often without clear definitions accompanying the terms.

Why This Matters for Service Providers

If you are selling or deploying AI voice agents to clients, your ability to speak clearly about how the technology works is a direct competitive advantage. Clients who hear terms like "hallucination" or "LLM" without context will either disengage or, worse, develop skepticism toward AI solutions broadly.

MSPs and telecom resellers are increasingly being asked by customers to explain AI capabilities, limitations, and risks. Understanding the difference between a model that is fine-tuned for a specific use case versus a general-purpose LLM, for example, directly informs how you should be positioning white-label voice agent products.

The most actionable takeaway: get your sales and support teams fluent in this terminology now. Customers are starting to ask harder questions, and the providers who can answer them clearly will earn more trust and close more deals.

Looking Ahead

As AI voice and automation tools become standard offerings in the MSP stack, technical literacy around AI concepts will shift from a differentiator to a baseline expectation. Start building internal training resources around these terms before your competitors do.

For the full story, read the original article on TechCrunch AI.