AI · LEGAL TECH

Vector Search Over US Case Law: How ULegal AI Finds Precedent

Inside the legal RAG pipeline ingesting thousands of case documents, building a Qdrant collection, and serving Claude 3.5 Sonnet-powered answers with citations.

Ahsan Iqbal

Lead Engineering Architect

Jun 30, 2026 10 min read

💡 Key Takeaways & Architecture Summary

Hierarchical indexing: Legal statutes are indexed at both document, section, and paragraph granularity.
Strict citation generation: Claude 3.5 Sonnet system prompts require explicit bracketed source attribution.
Qdrant payload filtering allows rapid scoping by jurisdiction, court level, and filing year.

The Challenge of Legal AI Precision

In legal research, a close answer is a wrong answer. ULegal AI processes millions of lines of US federal and state case law, requiring exact statutory references, verbatim quote validation, and strict jurisdiction filtering.

Qdrant Vector Indexing Architecture

We structured Qdrant payload filters to allow instantaneous slicing across circuit courts, judge opinions, and topic taxonomy before running nearest-neighbor vector math.

Tags:

#Legal AI

#Qdrant

#Claude 3.5

#Vector Search

#Python

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Vector Search Over US Case Law: How ULegal AI Finds Precedent

💡 Key Takeaways & Architecture Summary

The Challenge of Legal AI Precision

Qdrant Vector Indexing Architecture

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