# Retrieval-Augmented Generation to Generate Knowledge Assets and Creation of Action Drivers
###### Abstract  
This article explores the application of Retrieval-Augmented Generation (RAG) to enhance the creation of knowledge assets and develop actionable insights from complex datasets. It begins by contextualising the limitations of large language models (LLMs), notably their knowledge cut-offs and hallucination tendencies, and it will present RAG as a promising solution that integrates external knowledge retrieval to improve factual accuracy and relevance. This study reviews current RAG architectures, including naive and advanced models, emphasising techniques such as optimised indexing, query refinement, metadata utilisation, and the incorporation of autonomous AI agents in agentic RAG systems. Methodologies for effective data preprocessing, semantic-aware chunking, and retrieval strategies--such as multihop retrieval and reranking--are also discussed to address challenges such as irrelevant retrieval and semantic fragmentation. This work further examines embedding models, notably the use of state-of-the-art vector representations, to facilitate precise similarity searches within knowledge bases. A case study demonstrates the deployment of an RAG pipeline for analysing multisheet datasets, highlighting challenges in data structuring, prompt engineering, and ensuring output consistency.  
RAG; Retrieval-Augmented Generation; LLM; large language models; AI; artificial intelligence +
Footnote †: journal: Applied sciences  
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These challenges have made it difficult to consistently obtain factually accurate information. Retrieval-Augmented Generation (RAG) [4, 5] has emerged as a promising solution to address these limitations by augmenting LLM outputs with retrieved factual information. The effectiveness of RAG implementations depends heavily on both the nature of the input data and the methodologies used to organise and feed this information into the system [6]. Recent studies have shown that RAG effectiveness is significantly dependent on both the quality of knowledge retrieval and the complexity of the target task [7].  
This article explores the current state of the art in RAG technology and examines various approaches to its implementation, with a particular focus on optimising the creation of knowledge assets and developing actionable insights [8]. The main contribution of this research focusses on the implementation of an RAG-based system to address a real-world scenario, based on the Baseline Review 2024 for the Liverpool City Region [9], as discussed in Section 6, which provides an economic assessment of the Professional Business Services (PBS) sector. This review is based on large and diverse datasets, suggesting several challenges associated with the use of AI-based analysis. In this work, novel approaches are implemented to address data structuring, prompt development, and testing/retest for consistency.  
Evaluation metrics, including context relevance and answer faithfulness, are employed to assess system performance. The findings demonstrate that RAG systems, particularly when enhanced with AI agent capabilities for autonomous query optimisation and adaptive response generation, possess substantial potential to generate reliable, comparable, and insightful knowledge assets, thereby significantly advancing data-driven decision-making processes in complex organisational contexts and establishing practical frameworks for implementing intelligent information systems in real-world applications.