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Understanding Retrieval Augmented Generation (Rag)
TLDR Retrieval-Augmented Generation (RAG) offers a solution to the limitations of traditional language models by utilizing vector stores to retrieve and generate up-to-date information without the need for constant retraining. This approach allows for attribution of information sources and presents a promising alternative for handling user queries.
Understanding the Limitations of Traditional Language Models
Traditional language models face challenges in providing up-to-date information and source citations. They rely on pre-existing data and cannot dynamically update their knowledge, leading to outdated information upon deployment. This limitation creates time and cost constraints associated with constantly retraining models to stay relevant.
Implementing a Vector Store Approach
To address the limitations of traditional language models, a vector store approach is proposed. This approach involves storing embeddings of information and utilizing them for retrieval augmented generation. By connecting the model to a database for updating information, this approach aims to overcome the technical limitations presented by the models' lack of understanding large datasets of human language.
Advantages of Retrieval-Augmented Generation (RAG) Model
The RAG model offers the advantage of retrieving the latest information from a vector store and using it to generate responses, eliminating the need to retrain the model for every update. Additionally, it enables attribution of information sources, addressing the challenge of source citations. This approach provides a programmer's perspective by integrating vector stores to handle user queries and offer updated responses.
What is the challenge with traditional language models?
The challenge with traditional language models lies in their inability to provide up-to-date information and source citations.
What is the alternative solution proposed to update information in language models?
An alternative solution involves connecting the model to a database for updating information, but technical limitations arise from the language models' lack of understanding human data.
How does the RAG (Retrieval-Augmented Generation) model work?
The RAG model works by retrieving the latest information from a vector store and using it to generate responses.
What are the advantages of RAG?
One advantage of RAG is the ability to provide up-to-date information without the need to retrain the model. Additionally, the implementation of RAG allows for attribution of information sources.
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