What Is RAG (Retrieval Augmented Generation)?
RAG combines AI language models with authoritative knowledge retrieval to produce accurate, grounded outputs.
Cassandra Research editorial team
Research methodology: Check the cited research, model documentation and evaluation conditions before applying an AI technique to professional work.
Plain-English Explanation
Retrieval Augmented Generation (RAG) is a technique that makes AI smarter by giving it access to specific, authoritative information before it generates a response. Instead of relying only on what the AI learned during training, RAG first searches a curated knowledge base for relevant documents, then uses those documents as context to generate an answer that should be checked against those documents.
How RAG Works
Query Processing
The user's question is converted into a mathematical representation (embedding) that captures its meaning.
Retrieval
The system searches a vector database of authoritative documents to find the most relevant information.
Augmentation
Retrieved documents are provided to the language model as context alongside the original question.
Generation
The language model generates a response grounded in the retrieved documents, with citations.
Why It Matters
RAG can make source material available during generation and help a reviewer trace an answer. It does not guarantee accurate retrieval, correct citations or a sound conclusion. Check that the cited authority actually supports each claim.
Frequently Asked Questions
Does RAG eliminate AI hallucinations?
No. RAG can supply relevant source material, but retrieval failures, outdated documents and incorrect interpretation can still produce unsupported answers. Evaluate the system and verify its output.
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