RAG in Short · Data Alchemy

RAG in Short

I believe most of you know what RAG is. But some might not. I am personally very exited on the LLM development possibility of Graph RAG. So let me share what in short RAG is as a foundation.

Retrieval-Augmented Generation (RAG) is a popular method in AI that solves the problem of language models forgetting or not knowing specific or updated information, especially in real-world or specialized use cases. While traditional in-context learning only uses the few examples or prompts provided at the time of the query, RAG goes a step further by actively searching a connected knowledge base—like a set of documents or a company’s database—for relevant information, then feeding those results into the language model to generate more accurate and grounded answers. This makes RAG especially useful when you need reliable, source-based responses.

To implement it, you store your data in a searchable format (usually in a vector database), use an embedding model to match user questions with the most relevant chunks of information, and then give both the question and retrieved content to the language model to generate a smart, well-informed response.

See this information:

https://www.youtube.com/watch?v=T-D1OfcDW1M&t=18s

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