Large language models (LLMs) have demonstrated remarkable capabilities for many applications, generating convincing, natural-sounding text with impressive levels of fluency. However, LLMs are not without their shortcomings: One of the most significant limitations is their tendency to produce ‘hallucinations’, plausible sounding but factually incorrect statements, and a lack of traceability in how the model came to a given response. These issues pose challenges for organizations seeking to deploy solutions using LLMs in production environments where compliance, such as adherence to the EU AI Act, is crucial.

Let’s have a look at Retrieval-Augmented Generation (RAG), a technology designed to address these limitations by integrating domain-specific knowledge into an LLMs response. At its core, RAG combines the generative power of LLMs with information retrieval, that is, search in sets of documents or other resources. This approach allows RAG to consider relevant data from a specific domain or company’s knowledge base and passing it on to the LLM. The result is a response that is not only coherent, but contextually relevant and grounded in the specific knowledge pertinent to the domain or organization.

Note that the quality of the output of a RAG pipeline is heavily dependent on the quality and breadth of the underlying knowledge sources: If the sources are outdated or incomplete, the generated responses will be less accurate as well. Moreover, RAG must be carefully calibrated to balance the retrieval of facts with the creative generation of language, ensuring that the output remains natural and engaging, while still correctly representing the information found in the source documents.

To build solutions that are reliable, secure, and efficient, researchers and specialized AI companies such as Y.digital are exploring advanced RAG models and combinations of RAG with knowledge graph technology. Knowledge graphs offer a structured representation of knowledge, with entities and relationships between them that mirror real-world connections. By integrating RAG with knowledge graphs, it is possible to achieve a deeper understanding of context and relationships in the provided information, leading to more nuanced and accurate responses.

These approaches aim to create AI systems that not only excel in generating human-like text, but also provide traceable and compliant outputs. Such systems can be a valuable asset for organizations that operate under strict regulatory frameworks, ensuring that AI-generated content adheres to legal as well as ethical standards.

Would you like to know more about what AI can mean for your organization? We are happy to help: result@y.digital

‍