# All for law and law for all: Adaptive RAG Pipeline for Legal Research

arXiv: https://arxiv.org/abs/2508.13107 · HTML: https://mlsanity.com/paper/2508.13107/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Placed in

- Retrieval-augmented generation / [Legal and financial RAG](https://mlsanity.com/rag/legal-and-financial-rag/)

## Builds on (most cited first, 3 of 3)

- [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://mlsanity.com/paper/2403.14403/) (2024)
- [LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain](https://mlsanity.com/paper/2408.10343/) (2024)
- [HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications](https://mlsanity.com/paper/2409.09046/) (2024)

## Built on by (most cited first, 5 of 5)

- [Fine-grained Claim-level RAG Benchmark for Law](https://mlsanity.com/paper/2605.21071/) (2026)
- [Retrieving Semantically Similar Decisions under Noisy Institutional Labels: Robust Comparison of Embedding Methods](https://mlsanity.com/paper/2512.05681/) (2025)
- [Navigating Global AI Regulation: A Multi-Jurisdictional Retrieval-Augmented Generation System](https://mlsanity.com/paper/2604.25448/) (2026)
- [Maat: The Agentic Legal Research Assistant for Competition Protection](https://mlsanity.com/paper/2605.27331/) (2026)
- [Evaluating RAG for French immigration law: a benchmark and baseline study](https://mlsanity.com/paper/2607.24449/) (2026)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has transformed how we approach text generation tasks by grounding Large Language Model (LLM) outputs in retrieved knowledge. This capability is especially critical in the legal domain. In this work, we introduce a novel end-to-end RAG pipeline that improves upon previous baselines using three targeted enhancements: (i) a context-aware query translator that disentangles document references from natural-language questions and adapts retrieval depth and response style based on expertise and specificity, (ii) open-source retrieval strategies using SBERT and GTE embeddings that achieve substantial performance gains while remaining cost-efficient, and (iii) a comprehensive evaluation and generation framework that combines RAGAS, BERTScore-F1, and ROUGE-Recall to assess semantic alignment and faithfulness across models and prompt designs. Our results show that carefully designed open-source pipelines can rival proprietary approaches in retrieval quality, while a custom legal-grounded prompt consistently produces more faithful and contextually relevant answers than baseline prompting. Taken together, these contributions demonstrate the potential of task-aware, component-level tuning to deliver legally grounded, reproducible, and cost-effective RAG systems for legal research assistance.