Glossary
Retrieval-Augmented Generation
An AI technique that combines information retrieval from knowledge bases with language model generation to produce more accurate, grounded outputs.
What is Retrieval-Augmented Generation?
Retrieval-augmented generation (RAG) addresses language model limitations like hallucination and knowledge staleness by grounding generation in retrieved factual content. When responding to queries, RAG systems first search relevant knowledge sources, then provide retrieved information as context for generation. This grounds outputs in verifiable sources rather than relying solely on training data.
RAG implementations vary in complexity from simple document retrieval to sophisticated multi-stage processes involving query rewriting, semantic search, relevance ranking, and source attribution. Effective RAG systems balance retrieval precision with generation quality, ensuring outputs are both factually accurate and naturally expressed.
Example
A customer asks "What is your return policy for electronics?" The RAG-powered support agent retrieves the current electronics return policy from the company knowledge base, then generates a natural response grounded in that retrieved policy, including accurate details about the 30-day window and restocking fees.
How Signet addresses this
Signet's Quality dimension favors agents using RAG and similar grounding techniques because they demonstrate lower hallucination rates and better factual accuracy. Agents that cite sources and ground outputs in verified information earn higher Quality scores than those relying purely on generative capabilities.
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