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--- |
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datasets: |
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- chromadb/paul_graham_essay |
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language: |
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- en |
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tags: |
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- RAG |
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- Retrieval Augmented Generation |
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- llama-index |
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--- |
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# Summary: |
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Retrieval Augmented Generation (RAG) is a technique to specialize a language model with a specific knowledge domain by feeding in relevant data so that it can give better answers. |
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# Implemeting RAG(in a nutshell): |
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### 1. Ready/ Preprocess your input data: |
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Language Models see all the data as tokens and vectors. So we want to convert the data to be fed into the same format. |
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### 2. Feed the processed data to the Language Model. |
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### 3. Indexing the stored data that matches the context of the query. |
