Definition
The process of injecting retrieved, factual information into an LLM's prompt to constrain its generation to specific data, thereby reducing hallucinations. This involves a trade-off between higher factual density and the risk of exceeding the model's context window limits or increasing inference latency.
Anchoring model responses to external evidence rather than electrical safety or philosophical grounding.
"An open-book exam where the LLM is the student and the provided context is the only permitted textbook."
- Retrieval-Augmented Generation (RAG)(Primary implementation framework)
- Hallucination(The primary error state grounding seeks to mitigate)
- Context Window(The architectural bottleneck for grounding data)
- Source Attribution(The verification mechanism for grounded outputs)
Conceptual Overview
The process of injecting retrieved, factual information into an LLM's prompt to constrain its generation to specific data, thereby reducing hallucinations. This involves a trade-off between higher factual density and the risk of exceeding the model's context window limits or increasing inference latency.
Disambiguation
Anchoring model responses to external evidence rather than electrical safety or philosophical grounding.
Visual Analog
An open-book exam where the LLM is the student and the provided context is the only permitted textbook.