Legal-RAG systems prioritize precision and accuracy over speed, using domain-specific vectorization and contextual semantic matching algorithms to retrieve relevant legal information. Direct re-ranking via large language models (LLMs) is used to improve the quality of retrieval, even if it increases latency. The “Needle in a Haystack” test is used to evaluate the system’s ability to extract specific legal information from a large dataset.
Source: True Law
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