Implementation Demonstration of a Legal RAG Control Architecture

Technical Note / Implementation Architecture for Controlled Legal RAG Systems

Subtitle

Edge-based Legal Initial Support AI with Internal Legal Database, Query Separation, and Auditable RAG

Overview

This document presents an implementation demonstration of a RAG control architecture for legal initial support AI.

In recent years, RAG (Retrieval-Augmented Generation) in the legal domain has gained attention as a technology that retrieves laws, case law, and contracts, and connects LLM responses to supporting materials. However, in practical legal operations, simply retrieving relevant documents is insufficient. It is necessary to distinguish whether the input concerns "legal risk assessment," "inquiries about systems or procedures," or "statutory confirmation," and to control at what depth RAG should be executed and at which stage the process should return to human judgment.

In this implementation, an edge-based legal initial support AI was constructed by integrating an internal legal database, structured preprocessing, query phase separation, target statute anchoring, multi-lane retrieval, case law search support, and audit logging. The system does not automatically determine legal conclusions; instead, it assumes final human judgment and provides relevant laws and statutes, procedural overviews, process flows, missing facts, and points requiring confirmation.

The distinguishing feature of this research lies not in the construction of a legal RAG database itself, but in the control structure that determines under what conditions, at what depth, based on which evidence, and to what extent AI should process legal RAG, and from which point it should return to human decision-making. This enables separation and verification of search results, inference results, and raw JSON logs, thereby improving the ability to distinguish errors in retrieval and reasoning, as well as enhancing auditability.

This implementation is positioned as an applied example of semantic structure control, based on the foundational philosophy of the Thought Chemistry System, including previously published materials. Note that the current integrated version contains subsequent refinements and is not identical to previously published materials; therefore, prior publications are treated not as direct specification references but as prior disclosures of the underlying conceptual framework.

This publication is intended for research demonstration purposes. Use for research purposes is permitted; however, operational use in practice is not guaranteed. Commercial use and derivative commercial products require a separate licensing agreement.

Therefore, this architecture should be understood not merely as an enhancement of legal RAG, but as an implementation example of judgment control structures in legal AI systems.

Keywords

Legal AI, RAG, Legal Database, Auditable AI, Edge AI, Legal Support, Generative AI, Human-in-the-loop, Legal Technology, Retrieval-Augmented Generation