Retrieval-Augmented Generation (RAG)
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.
How It Works
Instead of relying solely on its training data, a RAG-enabled system:
- Takes your query
- Searches a curated knowledge base for relevant documents
- Retrieves the most relevant information
- Provides that information to the LLM as context
- Generates a response grounded in the retrieved content
Think of it as giving the AI a specific, curated library to consult before answering your question.
Benefits for Research
- Reduced hallucination: Responses are grounded in actual documents you control
- Domain specificity: Can be tailored to your organization's research repository
- Citation accuracy: The model can reference specific sources from your knowledge base
- Currency: Can include recent information not in the model's training data
Limitations
RAG reduces but does not eliminate hallucination. The model can still:
- Misinterpret retrieved information
- Combine information in incorrect ways
- Generate content that goes beyond what was retrieved
Practical Application
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.
Related Terms
Large Language Model (LLM)
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.
Hallucination
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.
Fine-Tuning
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.
Mentions in the Knowledge Hub
This term is referenced in the following articles:
Advanced AI Techniques for Research
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.
Building a UX Insights Repository: A ResearchOps Guide
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.