What Is RAG (Retrieval Augmented Generation)?
RAG combines AI language models with authoritative knowledge retrieval to produce accurate, grounded outputs.
Cassandra Research — AI Division
Research methodology: Validated against peer-reviewed AI research, NIST frameworks, and industry benchmarks.
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 accurate, well-sourced answers.
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 dramatically reduces AI hallucinations by grounding responses in verified source material. This is critical for professional applications in law, tax, and medicine where accuracy is non-negotiable. Cassandra Research's AI engines use RAG architecture to ensure every response is backed by authoritative sources.
Frequently Asked Questions
Does RAG eliminate AI hallucinations?
RAG significantly reduces hallucinations by grounding AI responses in authoritative sources, but it does not eliminate them entirely. Quality of the knowledge base and retrieval system are critical factors.
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