AI · Legal Domain
Legal AI LLM
Domain-specific large language model engineered for legal corpus analysis with adversarial robustness and hallucination mitigation.
The Challenge
Legal precision
at scale.
Legal professionals require AI systems that are simultaneously precise, grounded in authoritative sources, and resilient against adversarial manipulation. General-purpose LLMs hallucinate legal citations, misinterpret jurisdictional nuances, and remain vulnerable to prompt injection attacks that could compromise confidential case information.
The objective was to engineer a domain-restricted LLM system that answers legal queries exclusively from verified document corpus, cites sources accurately, and defends against adversarial prompt manipulation — all while maintaining sub-second response latency.
The Architecture
Adversarially
hardened RAG.
Document Ingestion
Legal documents processed through a multi-stage pipeline: PDF extraction, section-aware chunking that respects clause boundaries, metadata tagging (jurisdiction, date, case type), and vector embedding via SambaNova infrastructure.
PromptGuard V2
Every incoming query passes through PromptGuard V2 — a classifier that detects prompt injection attempts, jailbreak patterns, and data exfiltration probes. Malicious prompts are rejected before reaching the LLM inference layer.
Retrieval-Augmented Generation
Queries trigger ChromaDB similarity search against the legal vector store. Retrieved chunks are ranked by relevance, concatenated with the query as grounding context, and fed to the LLM with strict instructions to only cite retrieved sources.
Hallucination Mitigation
Post-generation validation cross-references LLM outputs against the retrieved chunks. Ungrounded claims are flagged, and confidence scores accompany each response to signal reliability.
The Outcome
Trusted
legal intelligence.
The system delivers grounded, source-cited legal responses with adversarial prompt rejection. The PromptGuard V2 layer ensures that no injection attack can bypass the retrieval-augmented pipeline. Deployed on SambaNova infrastructure for reliable, low-latency inference — making it viable for real-time legal research workflows.