THE SHORT ANSWER
By forcing the model to generate answers strictly from retrieved, verified factual documents rather than its parametric memory.
Flashcards
Q1
What is the role of embeddings in RAG?
Embeddings convert text into dense numerical vectors so that related concepts can be found via mathematical similarity.
Q2
How does chunk size affect retrieval accuracy?
Too large, and irrelevant text dilutes the context; too small, and the LLM lacks the surrounding information needed to understand the point.
Q3
Why is freshness crucial for RAG data stores?
Because out-of-date vector indices will successfully retrieve obsolete answers, making the AI confidently wrong based on old facts.
Related Concepts
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AI Summary
This page covers RAG and Grounded AI as a technical flashcard. Description: How does RAG prevent AI hallucinations?.