RAG AI application

A RAG (Retrieval-Augmented Generation) AI application enhances a Large Language Model (LLM) by retrieving relevant information from a specialized knowledge base before generating an answer. This process provides the LLM with timely, accurate, and contextually relevant data, enabling it to deliver more precise, trustworthy, and up-to-date responses, and even cite sources for verification.  

How RAG Works

RAG applications work in two main phases: 

1. Retrieval Phase:
   • A user submits a prompt or question to the RAG system. 
   • An information retrieval model queries a specific knowledge base (like internal documents or the internet) to find snippets of information relevant to the user’s prompt. 
   • These retrieved snippets are often converted into vector embeddings, which store their meaning, allowing for faster retrieval by meaning rather than just keywords. 
2.  Generation Phase:
   • The retrieved data is combined with the user’s original prompt to create an “augmented” prompt. 
   • The LLM receives this augmented prompt and uses the additional context to synthesize a response. 
   • The LLM’s response is then presented to the user, often with links to the original sources for further verification. 

Why RAG is important

• Increased Accuracy and Relevance: RAG ensures that the AI is not just relying on its potentially outdated training data but is also using current, specific information for a more accurate answer. 
• Reduced Hallucinations: By grounding responses in external sources, RAG helps to prevent the LLM from generating incorrect or misleading information. 
• Source Attribution: RAG allows the AI to provide citations for its answers, increasing user trust and enabling users to verify the information. 
• Domain-Specific Knowledge: RAG allows developers to connect LLMs to specialized, private, or proprietary datasets, making the AI more useful for specific industries or tasks. 
• Real-time Information: RAG can pull in information from live feeds, news sites, or other frequently updated sources, providing the most current data to users.