Technology
8 articles in this category
LLM Hallucination in Contract Analysis: Why Source Verification Is Non-Negotiable
Large language models hallucinate. In legal contract analysis, a single fabricated clause citation can derail a deal. Here is how hallucination manifests in legal AI, why it happens, and how to build systems that prevent it.
Amendment Chain Resolution: The Hardest Problem in Legal AI
Why amendment chains break standard AI document analysis approaches, how structured extraction handles them, and what makes multi-amendment resolution the defining technical challenge for legal AI systems.
How We Test Legal AI Accuracy: Mage's Benchmarking Methodology
An inside look at how Mage benchmarks the accuracy of its legal AI system. Covers test methodology, human reviewer comparison, confidence scoring, and why accuracy without a rigorous testing framework is just a marketing number.
Why We Built a Legal Document Classifier First
Why Mage built document classification before extraction, how document types determine extraction strategy, and why getting classification right is the prerequisite for everything else in legal AI.
Why We Do Not Let Users Write Prompts
Open prompt boxes in legal AI create security risks, accuracy problems, and inconsistent output. Constrained interfaces that encode domain expertise produce better results for M&A attorneys than flexible prompt engineering ever will.
Why Clause-Level Segmentation Changes Everything in Legal AI
Most legal AI tools analyze documents at the page or paragraph level. Clause-level segmentation, where the system understands individual provisions as discrete units, is the difference between approximate summaries and precise, verifiable extraction.
Model Fusion: Why a Single AI Model Is Not Enough for Legal Document Analysis
Why Mage uses multiple specialized AI models instead of relying on a single general-purpose model. Covers the limitations of single-model approaches, how model fusion works, and why ensemble methods deliver the precision that legal work demands.
Why RAG Fails for Legal Contract Review
Standard retrieval-augmented generation pipelines were designed for question-answering over static corpora. Legal contract review demands something fundamentally different: structured extraction across amendment chains, cross-references, and clause-level precision.