Retrieval augmented generation (opens new window) (RAG) was a major breakthrough in the domain of natural language processing (NLP), particularly f

Challenges of Scaling Retrieval-Augmented Generation Applications

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2024-04-23 18:30:15

Retrieval augmented generation (opens new window) (RAG) was a major breakthrough in the domain of natural language processing (NLP), particularly for the development of AI applications. RAG combines a large knowledge base (opens new window) and the linguistic capabilities of large language models (opens new window) (LLMs) with data retrieval capabilities. The ability to retrieve and use information in real time makes AI interactions more genuine and informed.

RAG has obviously improved the way users interact with AI. For example, LLM-powered chatbots (opens new window) can already handle complicated questions and tailor their responses to individual users. RAG applications enhance this by not just using the training data, but also by looking up up-to-date information during the interaction.

However, RAG applications work pretty well when used on a small scale but pose significant challenges when we try to scale them, such as managing the API and data storage costs, reducing latency and increasing throughput, efficiently searching across large knowledge bases, and ensuring user privacy.