RAG

RAG can stand for Retrieval-Augmented Generation or red, amber, green. 

Retrieval-Augmented Generation

• RAG is an AI framework that combines information retrieval with large language models (LLMs). 
• What is RAG and LLM?

AI Overview

Retrieval-augmented generation (RAG) is an AI technique that combines large language models (LLMs) with external knowledge sources. LLMs are AI models that can perform tasks like answering questions and translating languages. 

How does RAG work? 

1. RAG uses search algorithms to find relevant information from external sources.
2. The retrieved information is then incorporated into the LLM.
3. The LLM uses this information to generate a response.

Benefits of RAG

• More accurate responses: RAG can help LLMs provide more accurate, relevant, and up-to-date answers. 
• Cost-effective: RAG can improve LLM output without having to retrain the model. 
• Personalization: RAG can personalize user interactions by integrating customer data with the LLM's knowledge. 

Use cases

RAG can be used in a variety of contexts, including chatbots, customer service, project management, and risk assessment. 

Limitations of LLMs

LLMs are trained on large datasets, but these datasets are finite and may be outdated. RAG helps LLMs overcome these limitations by providing access to additional knowledge sources. 

Generative AI is experimental. Learn more

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