AI conversations are drowning in jargon. Terms like 'hallucination,' 'token,' 'agentic,' and 'RAG' get thrown around in articles and product announcements, but many readers are left nodding along without clarity on what these actually mean for practical use.
A hallucination, in AI terms, isn't random—it's a model confidently asserting false information because it pattern-matched to training data that suggests a plausible-sounding answer. Understanding this distinction matters: hallucinations aren't bugs to be fixed so much as fundamental constraints of how LLMs work.
Tokens are the atomic unit that models actually process. Understanding token counts matters directly to your wallet—API costs scale with token use, not word count. A sentence might be 5 words but 8 tokens depending on punctuation and word boundaries.
Agentic systems are models given the ability to take actions—call APIs, update databases, make decisions—based on natural language instructions. RAG (Retrieval-Augmented Generation) is a technique for giving models access to external knowledge bases without retraining. Both are increasingly central to how companies deploy LLMs.
