RAG
RAG is a combination of “search + LLM”: the model first retrieves relevant data from your database, then responds based on it. Fewer hallucinations.
RAG - Retrieval-Augmented Generation. Instead of relying on the “memory” of the model, you first look for the necessary pieces in your knowledge base (documents, articles, customer database), and then give them to the model as context - and it responds strictly according to them.
Why does a marketer need this? LLM does not have your data: your cases, your tone of voice, your benchmarks. RAG fixes it. I keep a database of my articles and cases - and when I generate content or answer a typical question, the model is based on my real numbers, and does not make things up.
RAG is the main cure for hallucinations. A model that was given facts lies noticeably less often than a model that was asked to “remember.” But RAG is not magic: if the database is garbage, the output is garbage.
Related terms
Where is it understood in practice?
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