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 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.

• Query: The RAG query process starts with an initial question or prompt, which is then refined by a rewriter to make the intent clearer or to improve its format. The refined prompt is given to a retriever, which pulls relevant pieces of information from a large collection of data. A reranker prioritizes the data pieces to find the most important bits, and top-ranked pieces are then processed by a large language model (LLM) to create a coherent and relevant response. The entire process ensures that the final answer is accurate and contextually relevant and directly addresses the user’s initial query.

Across the industry, RAG systems have become the preferred choice for domain-specific GenAI projects.

The Key to Higher-Performance RAG: Human Oversight

Human oversight can significantly improve RAG performance across four key areas: data quality, prompt and response management, algorithmic accuracy, and contextual understanding. 

In terms of data quality, human experts can regularly audit and update datasets, ensuring they remain structured, well-formatted, and complete. They can add missing contextual metadata and maintain consistency as data ages. Human reviewers can also validate knowledge before it’s added to vector stores, improving the accuracy of information chunks. 

For prompt and response management, humans can help systems accommodate a wide range of prompts by understanding the knowledge base and potential fringe use cases. They can refine the prompt rewriting process to capture user intent better. Additionally, human analysts can standardize response tone, style, and specificity, enhancing the overall user experience. 

Algorithmic shortcomings can be addressed through constant human tuning and testing. Experts can refine retrieval algorithms to ensure correct interpretation and use of data chunks. They can also consistently test ranking and reranking algorithms to make sure relevant information is prioritized and presented effectively. 

Lastly, human intervention is vital for improving contextual understanding. Experts can enforce guardrails and provide corrective feedback to prevent misinformation. They can help the system adhere to established guidelines and ensure responses remain contextually accurate and relevant.

Human expertise improves RAG systems by enhancing data quality, refining algorithms, and ensuring contextual accuracy. This ongoing human oversight leads to more reliable and useful AI responses.

As AI becomes more prevalent, the demand for high-quality, user-friendly AI products is growing. RAG is now essential for modern AI development and combining it with human expertise gives companies a competitive advantage in this rapidly evolving field.