What is RAG in AI search?
RAG means retrieval-augmented generation: the AI first finds relevant information, then uses it to write an answer.
The short version
RAG combines search with generation. Instead of relying only on the model's training data, the system retrieves documents, articles, database rows, or notes, then asks the model to answer using that material. This is how many AI search and enterprise knowledge tools work.
Why RAG exists
Models have cutoff dates and imperfect memory. Retrieval lets an AI answer with fresher or private information without retraining the model. It can also ground answers in sources that users can inspect.
Where RAG fails
RAG can fail if the search query is bad, the source ranking is weak, the retrieved snippets are stale, or the model ignores the evidence. A grounded answer still depends on retrieval quality.
RAG vs. normal search
Normal search returns links. RAG tries to synthesize an answer from retrieved content. That is more convenient, but it makes source transparency and citation quality more important.
What good RAG systems include
Strong systems use hybrid search, reranking, deduplication, source scoring, freshness checks, and answer-level citations. They also say when the retrieved evidence is thin.
Related questions to ask AskClash
- How does vector search work in RAG?
- Why do RAG systems still hallucinate?
- What is the difference between RAG and fine-tuning?