Make the Most of Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) is becoming the standard architecture for integrating Large Language Models (LLMs) with business applications by extending the context of LLMs with proprietary business data and logic. This approach is essential for enhancing the accuracy, reliability, and trustworthiness of LLMs, which are typically trained on publicly available datasets and thus lack access to specific internal data necessary for precise business tasks. RAG addresses the challenges of data cut-off dates and hallucinations by grounding LLM responses in real-time and context-specific information, which not only improves the quality of responses but also reduces the need for costly retraining or fine-tuning of LLMs. Additionally, RAG systems improve speed and efficiency by offloading data retrieval from internal to external sources, allowing LLMs to handle large data volumes more effectively. Examples of RAG systems in production include Perplexity, a web-based answer engine, Cursor, a coding assistant, and HeyCloud, an AI assistant for DevOps, all showcasing the practical application of RAG in various domains.