Introduction

Generative artificial intelligence and Large Language Models (LLMs) are revolutionizing contemporary legal practice. However, mere availability of these tools does not guarantee reliable results: it is essential to master the art of Legal Prompting, that is, the ability to formulate precise and structured instructions that enable obtaining legally relevant, accurate, and compliant outputs.

This article provides an exploration of techniques, risks, and best practices for the effective use of LLMs in the legal domain, with particular attention to GDPR compliance, professional ethics, and data security aspects. The analysis is part of the broader debate on the balance between technological innovation, protection of fundamental rights, and ethics in the digital age, addressing the challenges emerging from the interaction between artificial intelligence and legal practice.

This article is part of a broader research work on Legal Prompting currently under publication.

Legal Prompting is the set of techniques and methodologies for effectively interacting with AI language models in legal contexts. Unlike generic prompting, Legal Prompting requires:

  • Terminological precision: correct use of legal language
  • Normative references: accurate citation of laws, articles, and case law
  • Logical structuring: organization of information according to legal standards
  • Awareness of limitations: understanding of AI’s critical issues and risks

A well-structured legal prompt must include:

  1. Role/Persona: define the AI’s professional context
  2. Context: provide relevant case information
  3. Specific task: clearly indicate the objective
  4. Constraints: specify regulatory, ethical, and formal limits
  5. Output format: define the expected response structure
  6. Examples (when appropriate): provide reference models

Practical example:

You are an Italian contract lawyer with experience in commercial law.

TASK: Draft a unilateral non-disclosure agreement (NDA).

CONTEXT:
- Parties: ABC Law Firm (Disclosing Party) and XYZ Company LLC (Receiving Party)
- Subject: negotiations for IT service provision
- Jurisdiction: Italian law, Milan court

CONSTRAINTS:
- Confidentiality duration: 3 years from signing
- GDPR compliance Art. 6(1)(b) and Art. 28
- Penalty: €50,000 per violation
- Termination clause for serious breach

FORMAT: Formal Italian contract, 2-3 pages, standard structure.

2. Advanced Prompting Techniques

Prompt engineering techniques have evolved significantly in recent years, as documented by recent systematic surveys that have catalogued over 50 different methodologies. In the legal context, certain techniques have proven particularly effective in ensuring precision, traceability, and regulatory compliance.

2.1 Zero-Shot Prompting

In Zero-Shot Prompting, the LLM responds without preliminary examples, relying exclusively on provided instructions. Suitable for standardized tasks where the model already possesses specific competencies.

Advantages: speed, implementation simplicity
Limitations: greater risk of generic or imprecise outputs

2.2 Few-Shot Prompting

Few-Shot Prompting provides the LLM with 2-5 examples of correct input-output before the actual task. This technique is particularly effective for standardizing outputs in specific formats.

Application example:

Extract data from contracts in JSON format.

EXAMPLE 1:
Contract: "Consultancy between Alpha LLC and Beta Ltd., value €50,000, duration 12 months"
Output: {"parties": ["Alpha LLC", "Beta Ltd."], "value": 50000, "duration_months": 12}

EXAMPLE 2:
Contract: "NDA between Gamma Corp and Delta Inc., penalty €20,000"
Output: {"parties": ["Gamma Corp", "Delta Inc."], "penalty": 20000}

NOW EXTRACT:
[Real contract text]

2.3 Chain-of-Thought (CoT) Prompting

Chain-of-Thought Prompting requires the LLM to explicitly state the step-by-step reasoning before providing the conclusion. Essential for complex legal analyses.

Example:

Calculate the GDPR fine for this violation. Proceed step-by-step:

DATA:
- Annual revenue: €250M
- Violation: Art. 6(1) - lack of legal basis
- Data subjects involved: 15,000
- Duration: 14 months
- First violation, partial cooperation with Authority

REQUIRED REASONING:
1. Identify applicable sanctioning range (Art. 83 GDPR)
2. Calculate theoretical maximum (4% revenue vs €20M)
3. Evaluate aggravating/mitigating circumstances (Art. 83.2 GDPR)
4. Estimate probable range based on precedents
5. Final conclusion with justification

2.4 Self-Consistency and Cross-Verification

For critical tasks, it is advisable to generate 3-5 different responses (varying parameters like temperature) and compare them to identify consistencies and discrepancies.

3.1 What is RAG?

RAG (Retrieval-Augmented Generation) is a technique that combines information retrieval from external databases with text generation by the LLM. The goal is to reduce “hallucinations” by providing the model with real documents as context.

Operation: The LLM generates citations based on its parametric knowledge without verifying external sources.

Critical risk: Invents non-existent case law citations (hallucination rate: 17-33% according to Stanford Law School Study, 2025).

Legal usage: NEVER for pleadings, legal opinions, or documents intended for third parties.

3.2.2 Retrieval-Only RAG (SAFE with Validation)

Operation: The LLM searches exclusively in a verified and controlled database (e.g., law firm’s case law archive).

Advantages:

  • Zero inventions (closed database)
  • Fast semantic search (minutes vs hours)
  • Citable outputs with traceability

Critical requirement: Mandatory human validation on official source before use.

Use case: Internal case law research, semantic search of firm contracts.

3.2.3 Hybrid RAG with Validation (BALANCED)

Operation: The LLM suggests references from database + web with reliability level indication.

Output type:

[VERIFIED SOURCE ✅]: EU Reg. 679/2016 Art. 9
[TO BE VERIFIED ⚠️]: Privacy Authority Guidelines 2023
[NOT VERIFIABLE ❌]: legal-tech.com blog

Usage: Quick preliminary research, always with expert supervision.

3.3 Critical Limitations of RAG: Scientific Evidence 2024-2025

Despite commercial enthusiasm, scientific research highlights significant RAG limitations:

  1. Fixed Retrieval: retrieval of N fixed documents often irrelevant or conflicting
  2. Lack of critical evaluation: the LLM does not evaluate the relevance of retrieved information
  3. Confidence Paradox: with RAG, the LLM becomes more confident even when wrong (Google Research, December 2024)
  4. Dependency on Data Quality: “A RAG system is only as good as its knowledge base” (RSNA Medical AI Study, 2025)

Alarming data:

  • Stanford Law School Study (June 2025): Legal RAG systems hallucinate in 17-33% of cases
  • TechCrunch (May 2024): “RAG won’t solve hallucination problem”

3.4 Evolved RAG Architectures

Research is developing solutions to mitigate traditional RAG limitations:

  • Self-RAG: the LLM autonomously decides when to retrieve information
  • Adaptive-RAG: adapts retrieval to query complexity
  • GraphRAG: uses knowledge graph with explicit semantic relationships
  • Corrective RAG (CRAG): evaluates document quality before passing them to the LLM

4.1 Contract Draft Generation

Scenario: Drafting standard NDA for new collaboration.

Benefits: Acceleration of initial drafting, clause standardization, structural completeness.

Required validation: Complete review by lawyer, verification of specific regulatory compliance, adaptation to concrete case.

4.2 GDPR Compliance Analysis

Scenario: Verification of e-commerce website privacy policy.

Effective prompt:

You are a certified Data Protection Officer (DPO).

TASK: Verify Privacy Policy compliance with GDPR Art. 13.

METHODOLOGY:
1. Read the policy in full
2. For each Art. 13 GDPR element verify: presence, completeness (1-5), gaps
3. Identify ambiguous or non-compliant clauses
4. Propose specific corrections with suggested wording

OUTPUT: Markdown table + list of priority criticalities + draft integrations

[PRIVACY POLICY TEXT]

4.3 Structured Data Extraction (JSON)

Scenario: Extract information from 100+ contracts for CRM database.

Advantages: Parsing automation, data structuring, integration with management systems.

Type schema:

{
  "contract_type": "string",
  "parties": {"party_a": "string", "party_b": "string"},
  "signing_date": "YYYY-MM-DD",
  "economic_value": {"amount": number, "currency": "EUR"},
  "special_clauses": ["array"],
  "competent_court": "string",
  "penalties": {"present": boolean, "amount": number}
}

4.4 Case Law Research with Retrieval-Only RAG

Setup: Verified database of 2,000+ Supreme Court decisions on specific topic.

Advantages:

  • Semantic search (concepts) vs superficial keyword search
  • Time: 2-3 minutes vs 3-4 hours traditional research
  • Output with similarity score and links to original PDFs

Critical validation:

  1. Open case PDF from firm database
  2. Verify correspondence of summary with full text
  3. Check case number on official legal database
  4. Read complete reasoning
  5. Evaluate actual applicability to concrete case

4.5 Contract Due Diligence

Scenario: Startup acquisition, analysis of 20 supplier contracts.

Focus areas:

  • Change of Control: clauses requiring consent for assignment
  • Renewal/Termination: contracts expiring within 12 months
  • Penalties: significant amounts (>€50K)
  • Exclusivity: restrictions limiting operational flexibility
  • IP/Confidentiality: developed IP ownership
  • Regulatory compliance: GDPR, antitrust, sector-specific

Output: Summary table, executive summary (top 5 risks), deep dive HIGH risk contracts.

4.6 Multilingual Contract Translation

Criticality: Preserve legal terminological precision, manage civil law vs common law concepts without direct equivalents.

Best practice: LLM translation + native speaker legal expert review + key terms glossary.

5. GDPR, Compliance, and Professional Ethics

5.1 Applicable GDPR Principles (Art. 5)

  1. Lawfulness, fairness, transparency: Inform clients if data used in LLM
  2. Purpose limitation: Use LLM only for declared/compatible purposes
  3. Data minimization: Anonymize/pseudonymize before input
  4. Accuracy: Verify output to avoid inaccurate data
  5. Storage limitation: Delete conversations post-task
  6. Integrity and confidentiality: Local LLM or robust DPA with cloud provider

a) Data subject consent: Obtain explicit consent (problematic due to lawyer-client power imbalance)

b) Contract performance: LLM necessary to fulfill contractual obligations (not “nice to have”)

c) Legal obligation: Rare in LLM context

d-e) Vital interest / Public interest: Generally not applicable

f) Legitimate interest: Balance between firm needs and data subject rights (requires DPIA)

5.3 Privacy Risk Matrix for LLM Use

Data CategoryLocalCloud with DPACloud without DPA
AnonymousOKOKWARN
Common personalOKWARNNO
Special categories (Art.9)OKNO*NO
Criminal (Art.10)OKNO*NO
Professional secrecyOKNONO
Trade secretOKNONO

*Unless exceptional guarantees + thorough DPIA

Professional Secrecy:

The lawyer must maintain secrecy about everything learned in professional practice.

Implication: Using cloud LLM with client data may constitute violation of professional secrecy.

Diligence:

The lawyer must handle affairs with diligence.

Implication: Verifying LLM output is mandatory. Blindly trusting AI may constitute professional negligence.

5.5 AI Disclosure Obligations

Transparency in AI use for professional activities:

AI DISCLOSURE NOTICE

This document was prepared with the assistance of artificial 
intelligence (Large Language Model), in compliance with applicable 
AI disclosure regulations.

Model: [model name]
Type: [local/cloud]
Purpose: [analysis/draft/translation]

Document supervised, verified, and validated by the signing 
professional, who assumes full responsibility for the content.

[Signature]

5.6 Compliance Checklist Before LLM Use

Before using LLM with real data:

  • ☑️ Legal Basis (Art. 6) identified and documented
  • ☑️ Privacy Notice (Art. 13-14) updated with LLM use mention
  • ☑️ DPA (Art. 28) signed if cloud provider
  • ☑️ Security (Art. 32): encryption, authenticated access, audit log
  • ☑️ DPIA (Art. 35) evaluated and documented if necessary
  • ☑️ Extra-EU Transfers (Art. 44-49): guarantees implemented (SCCs, adequacy decision)
  • ☑️ Processing Records (Art. 30) updated with LLM activities

6. Open Source vs Cloud Models: Privacy First

Critical advantages:

  • Data under control (no sending to external servers)
  • Complete model audit capability
  • On-premise deployment for law firms and public administration
  • No cloud provider dependency
  • Total GDPR compliance (Art. 32 - security by design)
  • Fine-tuning capability on specialized legal corpus

Recommended models:

  • LLaMA 3.3 (70B): Advanced reasoning, 128K context
  • Qwen 3 (14B): Perfect JSON extraction
  • Gemma 3 (27B): Native multilingual, drafting
  • Phi 4 (14B): Quick checks, speed
  • QwQ (32B): Precise calculations and math (GDPR fines)
  • DeepSeek-R1 (7B): Chain-of-thought, educational

6.2 Local Environment Setup (Example: Ollama)

# 1. Ollama installation
curl -fsSL https://ollama.com/install.sh | sh

# 2. Storage configuration
export OLLAMA_MODELS=/Volumes/OllamaModels/models

# 3. Server startup
ollama serve &

# 4. Model download
ollama pull llama3.3:70b

# 5. Test
ollama run llama3.3:70b "Analyze GDPR Art. 13 compliance"

6.3 Cloud with Caution: When and How

Acceptable cloud use:

  • Completely anonymized data (no re-identification possible)
  • Testing and development with synthetic data
  • Internal brainstorming (no client data)

Minimum cloud requirements:

  • ☑️ Robust DPA (Data Processing Agreement) Art. 28 GDPR
  • ☑️ No-training clauses (data not used to train models)
  • ☑️ EU data residency
  • ☑️ End-to-end encryption
  • ☑️ Audit rights and inspections
  • ☑️ Sub-processor guarantees

7. Guardrails and Output Control

7.1 What is a Guardrail?

A guardrail is a post-generation control system that verifies LLM output before making it available to the end user.

7.2 Essential Guardrails for Law Firms

def check_legal_output(llm_response):
    # Guardrail 1: No personal data
    if contains_personal_data(llm_response):
        return "BLOCKED: output contains personal data"
    
    # Guardrail 2: No invented citations
    if contains_fake_citations(llm_response):
        return "WARNING: verify citations on official source"
    
    # Guardrail 3: No inappropriate language
    if contains_profanity(llm_response):
        return "BLOCKED: unprofessional language"
    
    # Guardrail 4: Verify legal consistency
    if legal_inconsistency_detected(llm_response):
        return "WARNING: possible legal inconsistency"
    
    return llm_response  # OK, pass
  • NeMo Guardrails (NVIDIA): Enterprise-grade framework
  • Guardrails AI (open source): Customizable, Python-based
  • Moderation API (OpenAI): Content filtering
  • LangChain OutputParsers: Output schema validation

8. Risks and Mitigations

RiskProbabilityImpactMitigation
Citation hallucinationHIGHCRITICALRetrieval-Only RAG + Human validation
GDPR data breachMEDIUMCRITICALLocal LLM or robust DPA + Encryption
Professional secrecy violationMEDIUMCRITICALAnonymization + Internal policies
Professional liabilityMEDIUMHIGHOutput supervision + Disclaimer
Output biasHIGHMEDIUMHuman review + Model diversification
Technology dependencyMEDIUMMEDIUMInternal competencies + Manual fallback

8.2 Operational Guidelines

WHAT TO DO:

  1. Use local LLM for sensitive/confidential data
  2. Implement Retrieval-Only RAG with verified database
  3. ALWAYS verify citations on official source
  4. Train team on LLM limitations and risks
  5. Document decision-making process (audit trail)
  6. Implement technical and organizational guardrails
  7. Update privacy notices and consents

WHAT TO AVOID:

  1. Blindly trusting generated citations
  2. Using generative RAG for outputs intended for third parties
  3. Skipping human validation to “save time”
  4. Entering unnecessary personal data

WHAT NEVER TO DO:

  1. Copy LLM citations directly into court documents without verification
  2. Rely on RAG for legal opinions without control
  3. Completely delegate legal research to LLM
  4. Use cloud LLM for professional secrecy without adequate guarantees
  1. Model specialization: LLMs fine-tuned on specific legal corpus (e.g., tax law, criminal, administrative)
  2. Evolved RAG integration: GraphRAG, Self-RAG for more accurate case law research. Recent frameworks integrate prompt engineering with multidimensional knowledge graphs to support complex legal dispute analysis, semantically connecting norms, precedents, and doctrine
  3. Multimodality: Analysis of scanned contracts, complex documents with images/diagrams
  4. AI agents: Multi-step systems for automated due diligence, continuous compliance monitoring
  5. Explainability: Greater transparency in AI reasoning for regulatory compliance
  6. Specialized competencies: The growing recognition of legal prompting’s importance is evidenced by the organization of dedicated international competitions, highlighting how the ability to formulate effective prompts is becoming a strategic competency for legal professionals

9.2 Necessary Competencies for Future Lawyers

  • Technical literacy: Understanding LLM fundamentals, limitations, bias
  • Prompt engineering: Ability to formulate effective instructions
  • Data protection: GDPR, privacy by design, risk management
  • Critical thinking: Output validation, error identification
  • AI ethics: Professional responsibility implications

10. Conclusions

Legal Prompting represents an essential strategic competency for legal professionals in the age of artificial intelligence. However, the effectiveness and safety of these technologies depend on three fundamental pillars:

  1. Technical competence: Mastery of prompting techniques, understanding LLM limitations, ability to implement secure RAG systems

  2. Regulatory compliance: Rigorous respect for GDPR, professional obligations, sector regulations (AI Act)

  3. Human supervision: Critical validation of outputs, maintenance of professional responsibility, management of fiduciary relationship with client

AI does not replace the lawyer but enhances their capabilities when used with awareness, methodological rigor, and full understanding of risks. The challenge for law firms is to develop a balanced approach that integrates technological innovation and protection of fundamental rights, transforming AI from potential threat to tool of professional excellence.

Bibliography and References

Main Regulatory References

Monographs and Reference Works

  • Fabiano, N. (2024). Intelligenza Artificiale, Privacy e Reti Neurali: L’equilibrio tra innovazione, conoscenza ed etica nell’era digitale [Artificial Intelligence, Privacy and Neural Networks: The Balance Between Innovation, Knowledge and Ethics in the Digital Age]. Available at: https://www.nicfab.eu/it/pages/bookai/

Academic Studies and Research

Industry Reports and Guidelines

International Events and Initiatives

Related Hashtag

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