# Retrieval-Augmented Generation to Generate Knowledge Assets and Creation of Action Drivers
## 2 Related Works  
Retrieval-Augmented Generation (RAG) has emerged as a key technique in the application of LLMs to business contexts. This approach complements the generative capabilities of LLMs with the factual grounding of external knowledge bases, addressing critical limitations of standalone LLMs such as knowledge cut-offs, hallucinations, and lack of domain specificity. The core principle of RAG involves an LLM retrieving relevant information from an external data source before generating a response to a query [4]. This mechanism allows businesses to leverage their proprietary data or up-to-date information sources, ensuring that the LLM's output is not only coherent and contextually appropriate, but also accurate and reflective of current knowledge. Current research indicates that RAG improves LLM performance by establishing responses in dynamically accessible content, thus improving reliability, a crucial factor in enterprise applications [10, 11]. The integration of RAG offers several distinct advantages for businesses, such as  
**Enhanced Accuracy and Reduced Hallucination. ** By grounding responses in verifiable external data, RAG significantly mitigates the tendency of LLMs to generate factually incorrect information. This is particularly vital in business domains where accuracy is paramount, such as finance, legal, and healthcare [12]. **Domain-Specific and Up-to-Date Knowledge. ** RAG enables LLMs to access and utilise current specialised information from enterprise-specific databases or real-time data feeds. This overcomes the static knowledge limitation of pretrained LLMs, allowing businesses to deploy AI solutions that are conversant with their unique operational context and the latest developments [11]. **Improved Traceability and Trust. ** Responses generated by RAG systems can often be linked back to the source documents used for retrieval. This traceability improves user trust and allows verification, which is essential for compliance and critical decision-making processes [11]. **Cost-Effective Customisation. ** Compared to the extensive resources required to fine-tune or retrain an entire LLM with new or proprietary data, RAG offers a more cost-effective and agile way to customise LLM outputs for specific business needs by updating the external knowledge base [12]. In [13], the authors proposed an automated customer service system for mobile operators utilising LLMs combined with a Retrieval-Augmented Generation (RAG) system. The research aims to improve the quality of automated customer service for customers of mobile operators. The authors conducted a comprehensive analysis of open-source LLMs and methods for model adaptation and pretraining. They designed the architecture and deployment diagram for the RAG system. The paper also describes the main stages of system training and provides performance evaluations for different LLMs. In [14], TrumorGPT, a generative artificial intelligence solution, is introduced to verify facts in the health domain. Its primary goal is to distinguish trumors, which are health-related rumours that turn out to be true, providing a crucial tool to differentiate between speculation and verified facts. TrumorGPT leverages a large language model (LLM) with few-shot learning for semantic health knowledge graph construction and semantic reasoning. To address common issues in LLMs, such as hallucinations and limitations of static training data, TrumorGPT incorporates graph-based Retrieval-Augmented Generation (GraphRAG). GraphRAG accesses and uses information from regularly updated semantic health knowledge graphs that contain the latest medical news and health information, ensuring that fact checking is based on the most recent data. However, the effectiveness of an RAG system is highly dependent on the quality of the retrieval mechanism. Poor retrieval can lead to suboptimal or incorrect responses generated [4]. Issues such as \"lost in the middle\", where models struggle to utilise information from long contexts, also affect retrieval.