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Building Your Own Legal Benchmarks for LLMs and Vendor AI Tools

Building Your Own Legal Benchmarks for LLMs and Vendor AI Tools

AI tools in legal tech are getting better, but vendor demos don’t always reflect real-world challenges, a model might perform well in a controlled environment but struggle with scanned documents, jurisdictional differences, or cross-referencing complex clauses. To truly evaluate AI’s usefulness, legal teams need internal benchmarks that test performance in their workflows, not just generic legal reasoning.

This article walks through how to build effective AI benchmarks, ensuring that LLMs and vendor tools are assessed against the specific challenges of your practice. I’ll outline a structured approach and finish with a practical example focused on commercial lease analysis across multiple jurisdictions.

1. Move Beyond Vendor Demos

AI tools often perform well in carefully curated environments but struggle with messy, real-world legal documents, so your internal benchmarks let you test against your actual workflows rather than idealised scenarios.

2. Ensure AI Aligns with Your Firm’s Needs

A tool built for general contract analysis may not work well for niche legal tasks like complex lease agreements, regulatory compliance, or multi-jurisdictional contract review. A custom benchmark ensures AI is tested on what actually matters to your team.

3. Reduce Compliance & Liability Risks

If AI is going to support decision-making in legal workflows, accuracy matters. Poorly tested AI tools can introduce errors, misinterpret clauses, or fail to consider jurisdictional nuances, leading to risk exposure. Internal benchmarks ensure AI meets your firm’s accuracy and reliability standards before deployment.

This sounds like hard work... shouldn’t AI vendors do the testing for us? Ideally, yes, but in reality, demos only show tools at their best, and public benchmarks don’t reflect the complexities of your firm’s workflows. A tool might handle basic contract review but fail when confronted with poorly scanned leases, jurisdiction-specific nuances, or complex cross-references.

By developing your own benchmarks, you ensure AI tools are tested on your documents, your legal tasks, and your standards before they’re trusted in real work.

Defining Your Benchmarking Process

A structured benchmark allows you to compare different models, vendor solutions, and internal AI tools on a level playing field. Here’s how to design one.

  • Define Key Use Cases : AI can assist with tasks like contract review, due diligence, and compliance checks, but which of these matters most to your firm?
  • Establish Success Criteria: Are you measuring accuracy, retrieval relevance, consistency, or processing speed? Setting clear success metrics ensures measurable results.

2. Build a Representative Dataset

  • Gather a Diverse Set of Documents: Your dataset should include real-world legal documents, including standard contracts, regulatory filings, and complex negotiated agreements.
  • Include Multiple Formats: Legal work involves Word docs, PDFs, scanned images, and handwritten annotations. AI must handle all of them effectively.
  • Annotation & Gold Standard: Work with legal experts to label key clauses, obligations, and jurisdictional references. This allows AI-generated results to be compared to human-validated answers.

3. Define Evaluation Metrics

  • Clause Extraction Accuracy: Can the AI correctly identify, extract, and classify key clauses?
  • Consistency & Reliability: Running the same query multiple times should yield the same accurate result.
  • Contextual Understanding: AI should apply legal reasoning rather than simply extracting text.
  • Efficiency: How quickly does the tool process large documents? Does it introduce latency in review workflows?

4. Test Visual Data Processing

  • OCR for Scanned Documents & Handwriting: AI should accurately extract text from PDFs and images, not just structured digital files.
  • Site Plans & Annotations: If a lease includes a property map or handwritten notes, does the AI correctly correlate visual data with contractual obligations?

5. Evaluate Both Standalone LLMs & Vendor Solutions

  • Standalone LLM Testing: Assess raw AI reasoning before vendor-added enhancements like RAG and OCR.
  • Vendor AI Performance: Vendors layer AI with data extraction, retrieval systems, tagging, and workflow automation. Test whether these actually improve performance or introduce new errors.

Adapt to Vendor Transparency Limits

  • If a vendor provides retrieval logs, check whether the AI accessed the correct sections.
  • If the tool is a black box, rely on output-based evaluation and then comparing AI responses to human-validated answers.

Practical Example: Benchmarking Commercial Lease Analysis

Let's say your firm operates in England, Scotland, New York, Texas, and Ohio and regularly reviews commercial leases. Each jurisdiction has different statutory frameworks, terminology, and obligations. Your AI benchmark must test how well the tool:

  • Extracts key clauses (rent review, break clauses, dispute resolution).
  • Handles jurisdictional differences ("full repairing and insuring lease" in England vs. US lease structures).
  • Integrates visual data (linking lease clauses to site plans).

1. Define Scope & Objectives

  • Key Clauses for Extraction
    • Break Clauses: Under what conditions can the lease be terminated?
    • Rent Review Mechanisms: Is rent adjusted via CPI, fixed increases, or market review?
    • Maintenance Responsibilities: Who maintains the property and common areas?
    • Dispute Resolution: Does the lease require arbitration, mediation, or litigation?
  • Jurisdiction-Specific Requirements
    • England & Scotland: Statutory protections (such as Landlord and Tenant Act 1954).
    • New York, Texas, Ohio: Lease obligations can vary significantly by state law.
  • Visual Data Interpretation
    • Does the AI correctly associate textual lease obligations with property maps?

2. Construct the Dataset

  • Collect a Diverse Set of Leases
    • Include retail, industrial, and office leases with varied structures.
    • Incorporate negotiated leases vs. standardised agreements.
  • Format Variety
    • Word and PDF files.
    • Scanned, redacted, and annotated leases.
    • Leases with site plans, property maps, and boundary diagrams.
  • Annotation & Gold Standard
    • Label key clauses, responsibilities, and jurisdictional requirements.
    • Annotate site plans to verify whether AI correctly links textual obligations to visual areas.

3. Testing & Evaluation

  • Clause Extraction Accuracy
    • Does the AI correctly extract and classify lease clauses?
    • Does it mislabel or fail to identify clauses in longer documents?
  • Contextual Reasoning
    • If a break clause references another section, does the AI retrieve and interpret it correctly?
  • Jurisdictional Adaptation
    • Test: "Does this break clause comply with Scottish tenancy laws?"
    • Verify whether the AI retrieves the correct jurisdictional framework.
  • Visual Data Alignment
    • If a lease states "Tenant is responsible for maintaining Area A", does the AI:
      • Correctly identify this obligation in the text?
      • Link it to the appropriate section of the site plan?
  • Output-Based RAG Validation
    • If the vendor tool uses RAG, verify whether it retrieves:
      • The correct lease sections.
      • The relevant statutory provisions.
    • If retrieval logs are unavailable, compare AI results to human-validated answers.

4. Report, Compare & Refine

  • Summarise Key Findings
    • Accuracy rates for clause extraction, jurisdictional interpretation, and visual data processing.
    • Identify consistent AI errors, such as misinterpreting maintenance clauses.
  • Standalone AI vs. Vendor Tools
    • Did vendor workflow automation features improve accuracy, or introduce errors?
  • Refine & Expand the Benchmark
    • Adjust the dataset to include more varied site plans and lease amendments.
    • Update benchmarks as lease laws and AI capabilities evolve.

A structured, real-world AI benchmark ensures that your firm tests AI tools in a way that aligns with actual legal practice, by evaluating clause extraction, jurisdictional interpretation, and visual data handling, you can separate genuinely useful AI tools from overhyped vendor offerings.

With a robust internal benchmark, you can adopt AI with confidence, ensuring it enhances efficiency without compromising legal accuracy or risk management.