RAG (Retrieval-Augmented Generation) is a technique that enhances the accuracy of large language models (LLMs) by integrating external knowledge with prompts. This paper evaluates RAG's effectiveness in reducing hallucinations, which are false or misleading responses generated by LLMs. The study compares RAG with standard LLMs using prompts designed to induce hallucinations. Results show that RAG increases accuracy in some cases, but can still be misled when prompts contradict the model's pre-trained understanding. The paper highlights the complex nature of hallucinations and the need for more robust solutions to ensure LLM reliability. The study involved 56 participants who evaluated the accuracy of responses generated by LLMs with and without context. The results showed that adding context significantly improved accuracy, with subjects indicating that the model correctly navigated the text to produce accurate responses approximately 94% of the time. In contrast, without context, only 7.31% were considered accurate, with the majority being hallucinations or unhelpful responses. Despite these improvements, the study identified several error types, including noisy context, mismatched instructions and context, context-based synthesis, unusual formatting, and incomplete context. These errors can lead to hallucinations, even when accurate context is provided. The paper also discusses the implications of these findings for the development of more trustworthy LLMs and the need for further research into the reliability of RAG systems. The study concludes that while RAG significantly improves accuracy, it may still struggle to provide accurate information in cases where the context provided falls beyond the model's training data. The findings emphasize the importance of context in enhancing the accuracy of LLM responses and the need for continued research into the reliability of RAG systems.RAG (Retrieval-Augmented Generation) is a technique that enhances the accuracy of large language models (LLMs) by integrating external knowledge with prompts. This paper evaluates RAG's effectiveness in reducing hallucinations, which are false or misleading responses generated by LLMs. The study compares RAG with standard LLMs using prompts designed to induce hallucinations. Results show that RAG increases accuracy in some cases, but can still be misled when prompts contradict the model's pre-trained understanding. The paper highlights the complex nature of hallucinations and the need for more robust solutions to ensure LLM reliability. The study involved 56 participants who evaluated the accuracy of responses generated by LLMs with and without context. The results showed that adding context significantly improved accuracy, with subjects indicating that the model correctly navigated the text to produce accurate responses approximately 94% of the time. In contrast, without context, only 7.31% were considered accurate, with the majority being hallucinations or unhelpful responses. Despite these improvements, the study identified several error types, including noisy context, mismatched instructions and context, context-based synthesis, unusual formatting, and incomplete context. These errors can lead to hallucinations, even when accurate context is provided. The paper also discusses the implications of these findings for the development of more trustworthy LLMs and the need for further research into the reliability of RAG systems. The study concludes that while RAG significantly improves accuracy, it may still struggle to provide accurate information in cases where the context provided falls beyond the model's training data. The findings emphasize the importance of context in enhancing the accuracy of LLM responses and the need for continued research into the reliability of RAG systems.