This is how AI reads your private documents without a single line of retrieval code. ?
Traditional databases need you to write a query to get data out. RAG flips this entirely.
Before documents are even stored, they're chunked and embedded — meaning their meaning is encoded as numbers. At retrieval time, your question is matched against those embeddings by similarity. The closest matches fill the AI's context window, and then it generates your answer.
No custom query logic. No SQL. Just semantic matching that scales. ?
Trade-off? Storage overhead is real. And it can't do COUNT or SUM like SQL can. But for enterprise knowledge bases? RAG is the move.
? Drop a question below if you want a deeper dive into embeddings or vector DBs.
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