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.
Definition: 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.
Retrieval-Augmented Generation (RAG) is an advanced technique that improves LLM accuracy by connecting the model to a specific database of documents or knowledge.
Instead of relying solely on its training data, a RAG-enabled system:
Think of it as giving the AI a specific, curated library to consult before answering your question.
RAG reduces but does not eliminate hallucination. The model can still:
RAG is particularly valuable for building research repositories where you want an AI assistant that can answer questions specifically about your organization's past studies, methodologies, and findings—rather than generating generic responses from its general training.
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.
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.
The process of further training a pre-trained LLM on a specialized dataset to alter its behavior or improve performance on specific tasks. A high-effort approach for large-scale, specialized needs.
This term is referenced in the following articles:
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.
As research practices mature, ad-hoc methods break down. Research Operations (ResearchOps) shifts focus from executing individual studies to building infrastructure that allows researchers to work efficiently and consistently at scale.