Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data. This is done by retrieving data/documents relevant to a question or task and providing them as context for the LLM.
Retrieval Augmented Generation (RAG) is revolutionizing the capabilities of Large Language Models (LLMs) such as GPT. By integrating external knowledge retrieval, RAG enhances LLMs’ contextual understanding and response generation.

🔍External Knowledge

RAG empowers LLMs to tap into external knowledge sources, including databases and pre-determined repositories. This access expands the model’s knowledge base, enabling it to provide more accurate and informed responses.

💡 Enhanced Context

Incorporating external knowledge allows LLMs to grasp nuanced contexts beyond their training data. This enrichment enables LLMs to generate responses that are not only linguistically accurate but also contextually relevant.

🎯 Improved Predictions

RAG significantly enhances prediction quality by feeding LLMs with real-time information during inference[2]. This ensures that the responses generated are up-to-date and reflect the latest information available.

In summary, RAG-based LLMs represent a powerful fusion of advanced language modeling with external knowledge retrieval, leading to more precise, context-aware, and reliable responses.

By Asif Raza