As the use of generative AI (GenAI) grows exponentially, developers have turned their attention to improving the technology. According to EMARKETER, nearly 117 million people in the U.S. are expected to use GenAI in 2025, a 1,400% increase over just 7.8 million users in 2022. More demand means more scrutiny and increased demand for higher-quality products, and to that end, developers are turning to retrieval augmented generation (RAG) – a technique that augments language model generation by incorporating external knowledge.
Improving RAG system performance is a major challenge malaysia whatsapp number data for today’s AI developers, and to do so, they’re increasingly turning to a powerful resource: human expertise. Here, we will cover the basics of how RAG systems work, an outline of the query process, and why integrating humans into RAG systems should be considered a necessity, not a choice.
The Core Components of RAG Systems
On a basic level, RAG systems focus on two main functions: breaking down information, or the ingestion process, and how systems answer questions, which is the query process:
Ingestion: This begins by splitting documents into smaller, more manageable pieces called chunks. These can be defined by fixed criteria such as the number of characters, sentences, or paragraphs. In a RAG system, each chunk is transformed into a format that it can use to find information. Getting the size of these chunks just right is important – smaller and more precise chunks improve the match between a user’s question and the information retrieved, while still maintaining a balance between covering enough information and being specific.