Highlights
- A quick three‑question “AI retro” helps judges see where AI actually saves time and where it doesn’t.
- It reveals common AI mistakes so chambers can avoid repeat cleanup and use the tools more confidently.
- By capturing the prompts that work best, judges build a reusable playbook that strengthens consistency over time.
AI is making its way into judicial chambers across the United States, and with good reason. Generative AI (GenAI) tools promise faster legal research, more efficient document review, and streamlined drafting. But the promise comes with a challenge: How can judges ensure they’re using these tools responsibly, effectively, and without compromising judicial independence?
Judge Scott Schlegel of the Louisiana Fifth Circuit Court of Appeal has developed a ten-phase framework for judicial AI use that answers that question with remarkable clarity. While each phase matters, Phase 9—the “post-decision review”—stands as a critical step that transforms AI from an experimental tool into a trusted assistant.
Phase 9 calls for judges to track efficiency metrics, conduct quality assessments, identify recurring GenAI error patterns, and refine successful prompts to build a chambers-specific library. In practice, this means implementing a simple, monthly “AI retro” built around three essential questions.
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The three questions that build judicial confidence
How this strengthens judicial independence
Moving from experimentation to mastery
The three questions that build judicial confidence
1. Did AI save time?
Did using GenAI actually reduce the time spent on this case? Track concrete metrics: Were there fewer revisions? Did AI help move from initial review to final disposition faster than traditional methods?
This shifts adoption from trial-and-error to data-driven decisions. Judges might discover that AI excels at summarizing procedural histories but struggles with nuanced statutory interpretation. That’s actionable intelligence.
When judges can document that AI assistance reduced drafting time by 30 percent on interlocutory appeals, they replace generalized worry with informed decisions about where to deploy the technology.
2. Where did it error?
Phase 9 focuses on spotting GenAI error patterns and adjust prompting. It’s not about catching every error in real time; Phase 8 of Judge Schlegel’s framework already covers full cite‑checks and human record verification. Phase 9 is about pattern recognition.
Did the AI consistently misunderstand a particular type of legal argument? Did it struggle with certain exhibits or procedural postures? Did it hallucinate citations in predictable situations? Documenting these patterns prevents the same cleanup work on the next case.
This step reinforces the Sedona Conference’s reminder that, as of February 2025, no GenAI tool has fully solved the problem of hallucinations—outputs that sound accurate but aren’t. Because GenAI can appear confident even when it’s wrong, Judge Schlegel stresses the importance of keeping humans firmly in control. A monthly retrospective helps maintain that verification mindset and builds shared knowledge about which tasks AI handles reliably and which require extra scrutiny.
3. What prompt will we reuse?
Phase 9 culminates in refining and documenting successful prompts; Phase 10 keeps a shared, versioned prompt library so good practices survive clerk rotations. This is where the retro becomes a playbook.
When a particular prompt produced excellent results, like “Create a timeline of facts based on the filings” or “Identify any facts that are disputed versus those that appear undisputed by the parties”, judges should document it. Tag it by task type. Note what made it effective. Over time, this library becomes chambers’ institutional memory, ensuring consistency as staff changes and workflows evolve.
How this strengthens judicial independence
The Sedona Conference emphasizes that judges remain responsible for any work produced in their name and must verify accuracy. The guidelines recognize that “judicial authority is vested solely in judicial officers, not in AI systems.”
The monthly retro reinforces these principles in practice. It keeps AI firmly in its proper role: a tool for organizing, summarizing, and aligning tone, not for legal reasoning or judicial decision-making. Judge Schlegel’s framework makes this clear: “The ‘human in the loop’ is an essential component in maintaining judicial independence.“
By tracking what works and what doesn’t, judges build clear guardrails around AI use. The retro provides evidence that they’re using AI deliberately, measuring its impact, and maintaining control over the judicial process. This goes beyond good practice as it strengthens defensibility and public confidence in the judiciary.
Starting an AI retro practice
Judge Schlegel’s framework is “intended to help judges leverage available GenAI tools while preserving the essential human elements of judicial decision-making: wisdom and independent, human judgment”. The monthly retro is how judges operationalize that intention.
For you, you can set aside 30 minutes each month to review the cases where they used AI assistance. The three questions provide structure:
- Did AI save time? Document the metrics.
- Where did it err? Capture the patterns.
- What prompt will we reuse? Build the library.
The practice is lightweight by design. It creates a feedback loop without adding significant administrative burden. More importantly, it transforms how chambers staff think about AI—from a mysterious black box to a tool they understand, measure, and improve.
Moving from experimentation to mastery
As Judge Herbert B. Dixon Jr. notes about the Sedona guidelines, “these guidelines are not the completion of a mission. They represent a starting point.” The same applies to AI implementation in judicial chambers.
You won’t have clear answers immediately, and that’s normal. Over time, you’ll recognize which approaches work best, spot potential issues early, and build data showing your AI use is thoughtful and effective.
Judge Schlegel designed his framework “to meet judges where they are,” recognizing most courts lack enterprise AI tools or dedicated tech staff. The three-question approach follows this same practical philosophy—it’s implementable today for real judicial chambers.
The opportunity is clear: AI can improve judicial efficiency without compromising independence. The path forward is equally straightforward: measure results, learn from mistakes, and build knowledge through deliberate practice. This isn’t just another implementation step; it’s what transforms AI from risky experiment into sustainable advantage.
