Automated Machine Learning to Enhance Knowledge Retrieval in Retrieval-Augmented Generation Pipelines

The emergence of Large Language Models (LLMs) has introduced exciting possibilities for applications in the digital health domain. However, their unpredictable nature necessitates the development of trustworthy strategies to prevent the generation of hallucinations. A common approach to address this challenge is using Retrieval-Augmented Generation (RAG), where text generation is supported by controlled knowledge injected into the prompts. Even with RAG, ensuring reliable and authoritative information generation requires further research. In a previous work, we presented an enhanced approach to the classic RAG pipeline, introducing an initial step where the LLM generates an enhanced query to support the retrieval step. Results showed that performances are highly sensitive to the techniques adopted for embedding queries and retrieving documents. Accordingly, in this paper, we experiment with a novel automated machine-learning approach to conduct extensive testing across various configurations and explore the retrieval module. Our findings highlight that the embedder and, especially, the retrieval strategies strongly impact the overall performance of the RAG pipeline.