metadata
datasets:
- chromadb/paul_graham_essay
language:
- en
tags:
- RAG
- Retrieval Augmented Generation
- llama-index
Summary:
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.
How does RAG works?
1. Ready/ Preprocess your input data i.e. tokenization & vectorization
2. Feed the processed data to the Language Model.
3. Indexing the stored data that matches the context of the query.