How to Handle Hallucinations
in Legal AI Systems

In 2023, a New York attorney submitted a brief citing six cases that did not exist. ChatGPT had generated them. The attorney was sanctioned, and the incident became the defining cautionary tale for legal AI adoption. But the problem isn't that LLMs were used. The problem is that no engineering safeguards existed between the LLM output and the court filing. This guide covers the 6 techniques that legal AI systems must implement to prevent hallucinations from reaching production.

Why LLMs Hallucinate in Legal Contexts

Understanding the root causes helps you engineer better defenses:

• Training data gaps: LLMs are trained on internet text. Much of case law is behind paywalls (LexisNexis, Westlaw). The model has incomplete legal knowledge and fills gaps with plausible-sounding fabrications.
• Citation format pattern matching: The model has learned the format of case citations (Name v. Name, Volume Reporter Page) and can generate strings that look like real citations but reference non-existent cases.
• Temporal cutoff: The model doesn't know about cases decided after its training cutoff. It might cite a case that was later overturned or a statute that was amended.
• Jurisdictional confusion: The model might cite a case from a different jurisdiction or apply the wrong standard because it doesn't maintain jurisdictional awareness.

Technique 1: RAG with Verified Legal Databases

Never let the LLM cite cases from its parametric memory. Ground every legal reference in a verified retrieval source. This means connecting your RAG pipeline to authoritative databases like Westlaw, LexisNexis, or your firm's internal case management system.

The prompt must explicitly instruct the model: "Only cite cases that appear in the provided context. If the context does not contain a relevant case, state that no relevant authority was found." This is the single most important safeguard. For enterprise RAG architectures that support this, read about multi-tenant RAG with Pinecone.

Technique 2: Citation Verification Pipeline

After the LLM generates a response, a separate post-processing pipeline extracts every citation and verifies it against a legal database API. This pipeline checks:

This three-layer verification catches the vast majority of hallucinated citations before they reach a human reviewer.

Technique 3: Confidence Scoring and Uncertainty Flagging

Every statement in the LLM output should carry a confidence indicator. Implement this by asking the model to self-assess: "For each legal claim, indicate whether it is directly supported by the provided sources, partially supported, or based on general legal knowledge."

Statements flagged as "general knowledge" (not grounded in retrieved sources) should be highlighted in the UI with a warning badge. This transparency lets the reviewing attorney know exactly which parts need manual verification.

Technique 4: Constrained Generation with Legal Ontologies

Instead of free-form text generation, constrain the LLM's output to match a legal ontology. For contract analysis, define the output schema: issue, applicable clause, relevant case law (from retrieval only), and risk assessment. The model fills in structured fields rather than writing prose, which dramatically reduces hallucination surface area.

Technique 5: Human-in-the-Loop Review Workflows

For high-stakes legal work, the AI should draft and the human should approve. Build the workflow so that the AI output is presented as a "draft" with inline citations, confidence indicators, and flagged uncertainties. The attorney reviews, edits, and explicitly approves before any output reaches a client or court.

This is where LangGraph's interrupt/resume capability is valuable. The agent pauses at the review step and resumes only after human approval.

Technique 6: Continuous Evaluation Against Gold Standards

Maintain a benchmark dataset of 200+ legal questions with verified correct answers and citations. Run your system against this benchmark weekly and track hallucination rates over time. Any model update, prompt change, or retrieval modification should be validated against this benchmark before deployment.

For evaluation methodology that goes beyond subjective assessment, see our guide on evaluating LLMs properly.

The Compliance Dimension: Navigating Regulatory Requirements

Legal AI systems must also satisfy regulatory requirements. The ABA's formal opinions on AI use require attorneys to understand the technology's limitations and maintain supervisory responsibility. Your system should support this by:

• Logging every AI interaction for audit trails
• Making the AI's reasoning transparent (not black-box)
• Clearly disclosing when AI was used in work product
• Maintaining data privacy (client information must not leak to API providers)

For healthcare-specific compliance requirements, see our companion piece on agentic workflows and HIPAA compliance.

Frequently Asked Questions