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Retrieval Augmented Generation
Retrieval-augmented generation, or RAG, retrieves external evidence, adds selected context to a model request and generates an output conditioned on that context.
What it means
RAG separates an external non-parametric source from the model's learned parameters. Implementations differ in ingestion, retrieval, context assembly, generation, Attribution and evaluation.
Why it matters
The workflow can use current or controlled sources without retraining the generator, but retrieval and generation can each fail. Retrieved evidence does not guarantee a faithful, complete or legally compliant answer.
Example
A support assistant retrieves the current warranty clauses, supplies them with the question to a model and returns an answer whose citations resolve to those clauses.
Common mistakes
Do not call Vector Search alone RAG, assume citations prove support or expose restricted passages in a prompt. RAG also does not replace AI Act transparency duties that apply to relevant providers or deployers from 2 August 2026.
How AYSA handles this
Signals reviewed
retrieved sources, answer claims, citation targets, access rules, required disclosure
Problem AYSA can identify
AYSA can compare supplied RAG outputs with controlled web sources and flag missing or unsupported evidence.
Recommendation prepared
The proposal corrects the website source or records a retrieval, generation, citation or disclosure issue for its owner.
Approval preview
The user reviews source passages, generated claims, citation coverage, disclosure context and proposed action.
Execution
AYSA can update approved WordPress sources; model, prompt and retrieval changes need authorized system access.
Verification
AYSA recrawls sources and reruns connected evidence and citation checks after approved changes.
Limits
AYSA cannot guarantee answer correctness, third-party citations, or legal compliance without a role-specific assessment.
Sources and further reading
- Lewis et al. — Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — Original research paper
- Amazon Bedrock — How knowledge bases work — Official platform documentation
- Amazon Bedrock — Retrieve and RetrieveAndGenerate — Official platform documentation
- European Commission — AI Act transparency obligations — Official regulatory guidance
Written by Marius Dosinescu. Reviewed by AYSA SEO Editorial Team · 2026-07-28 00:00:00.
Quick answers
Frequently asked questions
Does RAG guarantee that a generated answer is correct?
No. The retriever can miss evidence and the generator can misstate, omit or overextend what was retrieved.
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