Using Retrieval-Augmented Generation (RAG) for Question-Answering Task

The Retrieval-Augmented Generation (RAG) methodology is a powerful tool for improving Question-answering systems. Combining the generative capabilities of LLM with extraction mechanisms can significantly improve the accuracy and reliability of responses, especially in domains with high specificity.

Project plan

1. Cleaning the raw text
2. Building word embeddings
3. Initialization of the vector database
4. User query conversion and vector database search
5. Generating a response