Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is an AI framework that combines information retrieval and natural language generation. It retrieves relevant data from external sources and integrates it into AI-generated responses, enhancing context and accuracy.

Also known as: Retrieval-enhanced generation.

Comparisons

• RAG vs. NLG: RAG retrieves information dynamically, while NLG generates text from predefined data.

• RAG vs. Chatbot: RAG-powered systems can reference external databases, unlike static chatbots.

Pros

• Contextual responses: Enhances text generation with real-time data.

• Versatility: Suitable for applications like customer support and content creation.

• Accuracy: Reduces errors by retrieving factual information.

Cons

• Complexity: Requires integration with external data sources.

• Latency: Real-time retrieval can increase response times.

Example

A legal document assistant uses RAG to generate responses to legal queries by retrieving information from legal databases and presenting concise, AI-generated summaries.