Hallucination
Hallucination is when a language model generates text that sounds confident and plausible but is factually wrong—invented citations, fabricated statistics, nonexistent API endpoints. It happens because LLMs are not databases. They are pattern-completion engines that predict likely next tokens, and sometimes the likeliest continuation is a fluent lie. Hallucination rates vary by model, task, and domain: open-ended creative writing has different tolerances than legal research. Mitigation strategies include retrieval-augmented generation (grounding responses in source documents), chain-of-thought prompting (forcing the model to show its reasoning), and structured output validation. None of these eliminate hallucination entirely. Any system where an LLM's output reaches a customer, a contract, or a database without human review or automated verification is a system waiting to embarrass you.
Related terms:
Token
In large language models, a token is the basic unit of text—usually chunks of three to four characters—that the model reads and generates.
Chain-of-Thought
Chain-of-thought prompting, introduced by Google Research in 2022, transforms AI from an answer machine into a reasoning partner by explicitly modeling the...
Agentic Workflows
Agentic workflows are multi-step AI processes where the system autonomously plans, executes, and iterates tasks—researching, drafting, reviewing, and...