RAG Playground introduces a systematic framework for evaluating and optimizing RAG systems through hybrid search and structured prompting, achieving 72.7% accuracy in multi-metric testing.
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https://arxiv.org/abs/2412.12322
🤔 Original Problem:
→ Current RAG systems lack standardized evaluation methods and struggle with balancing semantic versus lexical matching in retrieval.
→ Traditional metrics fail to capture the nuanced requirements of combined retrieval and generation tasks.
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🛠️ Solution in this Paper:
→ The framework implements three retrieval strategies: naive vector search, reranking, and hybrid vector-keyword search.
→ It uses a 256-token chunk size with 50-token overlap for document processing.
→ Custom ReAct agents employ structured self-evaluation prompting for improved response quality.
→ A comprehensive evaluation system combines programmatic (25%), LLM-based (45%), and hybrid (30%) metrics.
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💡 Key Insights:
→ Hybrid search methods consistently outperform single-strategy approaches
→ Structured self-evaluation prompting significantly improves response quality
→ Model size impacts performance less than optimized retrieval strategies
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📊 Results:
→ Hybrid search achieved 72.7% pass rate with Qwen 2.5 model
→ Context faithfulness improved to 86% with hybrid approach
→ Numerical accuracy reached 83.9% with custom prompting
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