When an AI model generates plausible-sounding but factually incorrect or fabricated information. A natural consequence of how LLMs predict probable text rather than verify truth.
Definition: When an AI model generates plausible-sounding but factually incorrect or fabricated information. A natural consequence of how LLMs predict probable text rather than verify truth.
Hallucination refers to when a Large Language Model generates content that sounds convincing but is factually wrong, made up, or nonsensical.
LLMs are designed to generate plausible-sounding text based on statistical patterns, not to verify factual accuracy. When a model encounters a gap in its training data or an ambiguous prompt, it fills the gap with what sounds statistically likely—even if that content is fabricated.
This is not a bug; it is a fundamental characteristic of how these models work.
Hallucination is why AI output must always be validated by a human expert. In research contexts:
The risk of hallucination reinforces the researcher's role as critical validator of AI output.
An AI system trained on vast amounts of text to predict and generate human-like language. Best understood as a concept-transformation engine rather than a knowledge database.
A technique that enhances LLM responses by first retrieving relevant information from a specific knowledge base, then using that information to ground the model's output.
This term is referenced in the following articles:
AI changes what researchers do and how many are needed. Productivity gains are real, and teams are getting leaner. But the skills that remain essential, strategic thinking, stakeholder influence, methodological judgment, and ethical reasoning, are precisely the ones AI cannot replicate. No guarantees, but building those skills is the best bet you have.
AI is fundamentally changing what research roles look like. Some purely executional positions are already disappearing. Understanding what LLMs actually are, and are not, determines whether you use them effectively or get replaced by someone who does.
Beyond basic prompting, there are techniques that dramatically improve AI reliability: structured communication, using notes over transcripts, treating models as a committee of raters, and understanding when RAG or fine-tuning makes sense.