Lawyers as AI Evaluators, Not Prompt Engineers: The Key to Better Legal AI

As generative AI  makes inroads into the legal world, many attorneys have begun exploring methods of “prompt engineering”—essentially, crafting precise queries or instructions to coax better, more accurate outputs from language models. While prompt clarity does matter, an overemphasis on tinkering with prompts can distract from a more meaningful activity: evaluating and critiquing the AI’s outputs and using that feedback to fine-tune or improve the model.

In this blog post, we’ll discuss why lawyers should shift their focus away from constantly re-engineering prompts and toward systematically evaluating AI outputs. We’ll also explore why leveraging your legal expertise to critique and guide the model is far more powerful in the long run—especially when fine-tuning a model to better align with your specific needs.

1. Prompt Engineering Has Limitations

1. Surface-Level Tweaks
   Although carefully constructed prompts can yield better responses, they only go so far. You’ll typically reach a plateau where further minor adjustments don’t meaningfully improve the model’s output.
2. Generic vs. Domain-Specific
   Language models are trained on broad data and can lack context-specific nuances—especially for highly specialized legal tasks. Even the best-phrased prompt can’t make up for inherent knowledge gaps.
3. Iterative Overhead
   Lawyers already face time constraints. Constantly refining prompts can become a tedious exercise, with diminishing returns. If the model’s base capabilities aren’t aligned with your practice area, no amount of prompt tweaking will fully resolve fundamental issues in the output.

2. Lawyers Excel at Quality Evaluation

1. Subject-Matter Expertise
   By definition, attorneys are experts in the legal field. You know when an argument is weak, when a citation is incorrect, or when a contract clause is incomplete. This insight is gold when it comes to reviewing AI outputs.
2. Easy to Spot Errors
   A generative AI might produce text that seems coherent but is factually or legally off the mark. As a trained lawyer, you can quickly pinpoint inaccuracies and omissions. This feedback is invaluable to improving AI performance.
3. Strategic Oversight
   Lawyers understand not just the “what” of a legal document or research memo, but the “why.” Critiquing AI outputs from a strategic standpoint (e.g., how a judge might interpret an argument) goes beyond simple correctness. It steers the AI toward more effective, outcomes-focused content.

3. Turning Evaluation into Better AI: The Power of Fine-Tuning

a. Collecting and Curating Feedback

• Systematic Critique: Each time you review an AI-generated document, note specific improvements or errors:
  • Incorrect legal citations
  • Omitted statutory requirements
  • Superficial vs. in-depth analysis
• Aggregate Data: Over time, these critiques form a valuable dataset about where and how the AI falls short of your needs.

b. Fine-Tuning the Model

• Directing the AI’s Evolution: Rather than constantly reworking prompts, you can feed your curated feedback into a fine-tuning process (where available) to create a version of the model that’s more aligned with your legal domain.
• Narrowing the Knowledge Gap: Fine-tuning allows you to infuse the model with your specific knowledge base—be it nuanced case law, preferred drafting styles, or unique regulatory frameworks.
• Improving Accuracy Over Time: As you incorporate your critiques and best practices, the model’s outputs improve in clarity, accuracy, and relevance. This fosters a long-term, cumulative benefit rather than ad hoc prompt-by-prompt gains.

c. Avoiding Repetitive Errors

• Persistent Mistakes: If you rely solely on prompt adjustments, you may keep encountering the same errors. Fine-tuning and continuous feedback loops help the AI learn and not repeat the same oversights.
• Domain Adaptation: If your practice focuses on, say, employment law or complex commercial litigation, fine-tuning can ensure the model develops deeper expertise in those areas.

4. How to Shift Your Workflow Toward Output Evaluation

1. Set Clear Review Criteria
   Develop checklists or criteria that you use to evaluate any legal document: facts, relevant statutes, case citations, logical flow, and risk analysis. Apply these criteria each time you critique an AI output.
2. Document Common Mistakes
   Keep a running log of recurring issues—like missing disclaimers in a contract or incomplete discussions of legislative history. These patterns will form the backbone of a fine-tuning dataset.
3. Iterate Strategically
   Instead of re-promoting ad infinitum, test a single robust prompt. Then evaluate the output thoroughly, capturing your critiques. Update your fine-tuning corpus with these critiques or direct feedback.
4. Invest in the Right Tools
   Some AI platforms offer native fine-tuning or “custom model” features—look for these. Others allow for retrieval-augmented generation (RAG), where you can supply your own documents or knowledge base.

5. The Bigger Picture: Why It Matters

1. Time Efficiency
   Lawyers are paid for their expertise, not for wrestling with prompts. A systematic evaluation and fine-tuning process spares you from repeated prompt experimentation.
2. Greater Consistency
   Once the model has “learned” from your feedback, it will consistently produce better outputs without needing continuous re-instruction.
3. Client Trust and Value
   Clients are increasingly aware of AI’s potential in legal services. By actively improving the AI’s reliability—through evaluation and fine-tuning—you’re delivering more accurate results and showcasing innovation.
4. Ethical Considerations
   Relying on AI in the legal profession raises concerns about accuracy, confidentiality, and liability. Hands-on evaluation coupled with fine-tuning ensures that you remain the ultimate arbiter of quality and compliance.

Conclusion

While prompt engineering can help refine AI-generated text, it’s no substitute for systematic output evaluation—especially in a field where precision and accountability are paramount. Lawyers bring indispensable expertise to the table, making them ideally positioned to critique AI outputs and guide language models toward more accurate, domain-specific content.

If you find yourself continually reworking prompts yet still dissatisfied with the results, consider investing time and resources into quality evaluation and fine-tuning. By channeling your insights into the model itself, you’ll enjoy a more reliable, efficient, and legally robust tool that enhances—not hinders—your legal practice. Ultimately, the real superpower here isn’t wrangling prompts, but harnessing your legal skill to shape AI into a better, more accurate assistant.

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Note: If you are evaluating your firm's AI strategy, we can help. Please reach out to info@truelaw.ai

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