Summary of Rag Playground: a Framework For Systematic Evaluation Of Retrieval Strategies and Prompt Engineering in Rag Systems, by Ioannis Papadimitriou et al.
RAG Playground: A Framework for Systematic Evaluation of Retrieval Strategies and Prompt Engineering in RAG Systems
by Ioannis Papadimitriou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis, Kompatsiaris
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce RAG Playground, an open-source framework for evaluating Retrieval-Augmented Generation (RAG) systems. The framework combines three retrieval approaches (naive vector search, reranking, and hybrid vector-keyword search) with ReAct agents using different prompting strategies. The authors provide a comprehensive evaluation framework with novel metrics and compare the performance of two language models (Llama 3.1 and Qwen 2.5) across various retrieval configurations. The results show significant improvements through hybrid search methods and structured self-evaluation prompting, achieving up to 72.7% pass rate on the multi-metric evaluation framework. The study highlights the importance of prompt engineering in RAG systems, with custom-prompted agents showing consistent improvements in retrieval accuracy and response quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG Playground is a new tool that helps scientists evaluate how well computer programs can create text based on what they find online. The program uses three different methods to search for relevant information and then generates text based on what it finds. The researchers tested two different language models, Llama 3.1 and Qwen 2.5, using these different methods. They found that combining the methods improved performance and also showed that tweaking how the program is prompted can make a big difference. |
Keywords
» Artificial intelligence » Llama » Prompt » Prompting » Rag » Retrieval augmented generation
