AI_dev Europe 2024

The incorporation of Retrieval Augmented Generation (RAG) has shown significant promise in mitigating hallucinations and bolstering Large Language Model (LLM) applications, especially within enterprise-based data realms. Although various approaches can be adopted for the retriever layer, the fusion of Knowledge Graphs with RAG has showcased improved grounding and augmented explainability. As the use of RAG escalates, coupled with the different configuration possibilities, a necessity for a standardised evaluation process to gauge the RAG pipeline performance arises. Such assessment remains a daunting task, characterised by time, effort, and cost implications. Current trends lean towards utilising LLMs to minimise human intervention and to score the performance of RAG pipelines. This talk provides an overview of the leading LLM-based tools and frameworks for the automated assessment of RAG systems. It also showcases an application utilising Neo4j-backed RAG pipelines with open-source libraries that shed light on the practical implications of vector and graph-based searches for the retriever layer and their evaluation using the open-source RAG Automated Assessment (RAGAS) framework.