Legal RAG Bench

Name: Legal RAG Bench
Availability: InStock
Author: beowa

by beowa·Feb 20, 2026·4 points·1 comment

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AI Analysis

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Legal RAG benchmark revealing embedding quality > LLM choice by 19-point margin.

Strengths

•Rigorous error decomposition methodology separating hallucinations from retrieval failures—unlocks actionable insights.
•Transparent, reproducible: openly released on HuggingFace with interactive explorer and statistical analysis.
•Challenges conventional wisdom: embedding models dominate RAG perf, not reasoning—directly useful for practitioners.

Weaknesses

•Narrow domain: legal RAG only; general RAG builders can't directly apply findings.
•Dependency risk: heavily features Kanon 2 Embedder, which appears to be author's own product.

Post Description

Hey HN, This is Legal RAG Bench, the first benchmark for legal RAG systems to simultaneously evaluate hallucinations, retrieval failures, and reasoning errors.

The key takeaways of our benchmark are: 1. Embedding models, not generative models, are the primary driver of RAG accuracy. Switching from a general-purpose embedder like OpenAI's Text Embedding 3 Large to a legal domain embedder like Kanon 2 Embedder can raise accuracy by ~19 points. 2. Hallucinations are often triggered by retrieval failures. Fix your retrieval stack, and, in most cases, you end up fixing hallucinations. 3. Once you have a solid legal retrieval engine, it doesn’t matter as much what generative model you use; GPT-5.2 and Gemini 3.1 Pro perform relatively similarly, with Gemini 3.1 Pro achieving slightly better accuracy at the cost of more hallucinations. 4. Google's latest LLM, Gemini 3.1 Pro, is actually a bit worse than its predecessor at legal RAG, achieving 79.3% accuracy instead of 80.3%.

These findings confirm what we already suspected, that information retrieval sets the ceiling on the accuracy of legal RAG systems. It doesn’t matter how smart you are, you aren’t going to magically know what the penalty is for speeding in California without access to an up-to-date copy of the California Vehicle Code.

Even still, to our knowledge, we’re the first to actually show this empirically.

Unfortunately, as we highlight in our write-up, high-quality open legal benchmarks like Legal RAG Bench and our earlier Massive Legal Embedding Benchmark (MLEB) are few and far between.

We point out, for example, that the popular Vals AI CaseLaw (v2) benchmark yields LLM rankings inexplicably and starkly different from ours while also failing to properly evaluate end-to-end RAG performance. Because CaseLaw (v2) is a private and proprietary benchmark, we are unable to confirm the source of the discrepancies we discovered, though we suspect they lie in a seriously flawed evaluation and labeling methodology.

In the interests of transparency, we have not only detailed exactly how we built Legal RAG Bench, but we’ve also released all of our data openly on Hugging Face here: https://huggingface.co/datasets/isaacus/legal-rag-bench/. We will also soon be publishing our write up as a paper.