Generative AI-Based Virtual Assistant using Retrieval-Augmented Generation: An evaluation study for bachelor projects
📰 ArXiv cs.AI
Learn how to build a Generative AI-Based Virtual Assistant using Retrieval-Augmented Generation to improve response accuracy and context-specificity
Action Steps
- Build a Retrieval-Augmented Generation model using a large language model as the base
- Configure the model to retrieve relevant information from a knowledge base
- Test the model's response accuracy and context-specificity using a evaluation dataset
- Compare the performance of the Retrieval-Augmented Generation model with a traditional language model
- Apply the findings to improve the development of virtual assistants in specialized content domains
Who Needs to Know This
This study benefits AI engineers, data scientists, and software developers working on virtual assistant projects, as it provides an evaluation of the effectiveness of Retrieval-Augmented Generation in improving response accuracy
Key Insight
💡 Retrieval-Augmented Generation can improve the accuracy and context-specificity of virtual assistant responses by retrieving relevant information from a knowledge base
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🤖 Improve virtual assistant response accuracy with Retrieval-Augmented Generation! 📊
Key Takeaways
Learn how to build a Generative AI-Based Virtual Assistant using Retrieval-Augmented Generation to improve response accuracy and context-specificity
Full Article
Title: Generative AI-Based Virtual Assistant using Retrieval-Augmented Generation: An evaluation study for bachelor projects
Abstract:
arXiv:2604.25924v1 Announce Type: cross Abstract: Large Language Models have been increasingly employed in the creation of Virtual Assistants due to their ability to generate human-like text and handle complex inquiries. While these models hold great promise, challenges such as hallucinations, missing information, and the difficulty of providing accurate and context-specific responses persist, particularly when applied to highly specialized content domains. In this paper, we focus on addressing
Abstract:
arXiv:2604.25924v1 Announce Type: cross Abstract: Large Language Models have been increasingly employed in the creation of Virtual Assistants due to their ability to generate human-like text and handle complex inquiries. While these models hold great promise, challenges such as hallucinations, missing information, and the difficulty of providing accurate and context-specific responses persist, particularly when applied to highly specialized content domains. In this paper, we focus on addressing
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