RAG System best practices

Might come with a lot of technical terms but, for any of you that have or are building RAG systems with LLMs, this research paper might be of your interest. Link to paper below.

This study investigates methods to improve RAG systems. The authors examined key factors influencing response quality, including language model size, prompt design, and knowledge base characteristics. Some new approaches are introduced and assessed such as query expansion, contrastive in-context learning, and focus mode retrieval. Through experimentation using the TruthfulQA and MMLU datasets, they identified best practices for RAG system design. The study finds that contrastive in-context learning and focus mode retrieval significantly enhance performance, providing insights for building more effective and adaptable RAG frameworks. Overall, the study highlights the importance of high-quality knowledge and tailored prompts for optimal results.

Link to paper: https://arxiv.org/abs/2501.07391?s=03

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