Highlights
- A concise three-question “AI retro” enables judges to identify where AI effectively saves time and where it falls short.
- This approach highlights common AI pitfalls, allowing chambers to avoid repeated errors and utilize the tools with greater assurance.
- By documenting effective prompts, judges create a reusable playbook that enhances consistency over time.
As artificial intelligence (AI) integrates into court systems across the United States, its benefits are increasingly evident. Generative AI tools offer the promise of swifter legal research, more efficient document review, and improved drafting processes. However, the challenge remains: how can judges ensure the responsible and effective use of these technologies without undermining their autonomy?
Judge Scott Schlegel of the Louisiana Fifth Circuit Court of Appeal has crafted a comprehensive ten-phase framework for judicial AI deployment that provides clear guidance. Among the various phases, Phase 9—“post-decision review”—is particularly pivotal as it transforms AI from a trial tool into a reliable assistant.
Phase 9 emphasizes the tracking of efficiency metrics, quality assessments, detection of recurring GenAI errors, and the refinement of successful prompts to establish a tailored library for the chambers. This practical implementation includes a straightforward monthly “AI retro” based on three fundamental questions.
Jump to ↓
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 the use of GenAI genuinely reduce the time dedicated to this case? It is essential to track specific metrics: were revisions minimized? Did AI facilitate a quicker transition from initial review to final decision compared to conventional methods?
This transition empowers judges to shift from a trial-and-error approach to decisions supported by data. They may find that AI is proficient at summarizing procedural histories but encounters difficulties with complex statutory interpretations. This insight is invaluable.
When judges can demonstrate that AI assistance cut drafting time by 30 percent in interlocutory appeals, they replace generalized concerns with informed decisions on where to utilize the technology.
2. Where did it error?
Phase 9 focuses on identifying recurring GenAI error patterns and refining prompting strategies. It’s not about catching every mistake in real time; earlier phases of Judge Schlegel’s framework have addressed complete cite-checks and human record validation. This phase centers on pattern recognition.
Did the AI consistently misinterpret certain legal arguments? Were there specific exhibits or procedural positions it struggled with? Did it fabricate citations in predictable circumstances? By documenting these patterns, judges can avoid repeating the same cleanup work in future cases.
This step emphasizes the reminder from the Sedona Conference, which notes that as of February 2025, no GenAI tool has completely resolved the issue of hallucinations—outputs that appear accurate but are not. Given that GenAI can project confidence even when it is incorrect, Judge Schlegel underscores the importance of maintaining human oversight. A monthly retro fosters a verification mindset and cultivates shared knowledge about which tasks AI manages reliably and which require additional scrutiny.
3. What prompt will we reuse?
In Phase 9, the focus shifts to refining and documenting successful prompts, while Phase 10 involves maintaining a shared, versioned prompt library to ensure best practices endure through clerk transitions. This is when the retro evolves into a strategic playbook.
When a specific prompt yields outstanding results, such as “Create a timeline of facts based on the filings” or “Identify any facts that are disputed versus those that seem undisputed by the parties,” judges should document it, categorizing it by task type and noting its effectiveness. Over time, this repository becomes the institutional memory of the chambers, ensuring consistency despite staff changes and evolving workflows.
How this strengthens judicial independence
The Sedona Conference emphasizes that judges remain accountable for all work produced in their name and must validate its accuracy. The guidelines clarify that “judicial authority is vested solely in judicial officers, not in AI systems.”
The monthly retro solidifies these principles in practice. It ensures that AI maintains its intended role: as a tool for organization, summarization, and tone alignment, rather than for legal reasoning or decision-making. Judge Schlegel’s framework states, “The ‘human in the loop’ is essential for maintaining judicial independence.“
By monitoring what succeeds and what doesn’t, judges establish clear parameters around the use of AI. The retro provides evidence of deliberate AI use, measuring its effects and safeguarding control over the judicial process. This proactive approach not only ensures best practices but also enhances public trust in the judiciary.
Starting an AI retro practice
Judge Schlegel’s framework is designed to assist judges in leveraging available GenAI tools while preserving the crucial human aspects of judicial decision-making: wisdom and independent judgment. The monthly retro serves as the practical application of this intention.
Dedicate 30 minutes each month for a review of cases that involved AI assistance. The three guiding questions provide a structured approach:
-
- Did AI save time? Document the metrics.
- Where did it err? Capture the patterns.
- What prompt will we reuse? Build the library.
This practice is intentionally lightweight, fostering a feedback loop without imposing significant administrative demands. More crucially, it shifts the perspective of chamber staff regarding AI—from a complex enigma to an understandable, measurable, and improvable resource.
Moving from experimentation to mastery
As stated by Judge Herbert B. Dixon Jr. regarding the Sedona guidelines, “these guidelines are not the conclusion of a mission. They signify a starting point.” This sentiment applies equally to AI implementation in judicial environments.
Immediate answers may not be forthcoming, which is entirely normal. Over time, judges will discern the most effective methods, preemptively identify potential problems, and build a data-supported case illustrating that their use of AI is both thoughtful and impactful.
Judge Schlegel tailored his framework to “meet judges where they are,” acknowledging that many courts lack specialized AI tools or dedicated technical staff. The three-question methodology embodies this practicality—it can be readily implemented in real judicial settings.
The potential is undeniable: AI can enhance judicial efficiency without jeopardizing independence. The route forward is equally clear: measure outcomes, learn from missteps, and cultivate knowledge through intentional practice. This approach transcends simple implementation; it transforms AI from a precarious experiment into a sustainable advantage.
