Book Review: Craig Ball, “The Leery Lawyer’s guide to AI and LLMs in Trial Practice”

Craig Ball’s “The Leery Lawyer’s Guide to AI and LLMs in Trial Practice” (2026), is available for free from his blog, 2026 Guide to AI and LLMs in Trial Practice | Ball in your Court.

Craig is a national resource.  A list of his contributions to this field would fill a book. Craig ran the prestigious Georgetown E-Discovery Training Academy, a week-long intensive ESI program. He has served as a special master, a consultant in computer forensics, forensic expert, law school professor, lecturer, writer, and trial lawyer.  We have worked on publications together, taught together at the Judicial College of Maryland, served on the ESI Principles subcommittee for the U.S. District Court for the District of Maryland together, and otherwise had many good times, such as his guest lectures to my University of Baltimore Law School ESI class.

Craig’s latest contribution is “The Leery Lawyer’s Guide to AI.”  In her blog Review: How “The Leery Lawyer’s Guide to AI and LLMs in Trial Practice” Made Me a More Confident Legal Tech User – EDRM (Jan. 13, 2026), Sheila Grela  wrote:

Many AI articles are steeped in jargon and abstract promises. So when I came across Craig Ball’s The Leery Lawyer’s Guide to AI and LLMs in Trial Practice, I found something different and extremely useful. This is not theory dressed up in tech-speak; it’s a practical manual for professionals navigating real legal work…. One thing that stood out immediately was how grounded the guide is. It skips the usual tech jargon and focuses on what matters: helping lawyers and their teams work more efficiently without compromising quality.

I agree.

In 2026 Guide to AI and LLMs in Trial Practice by Craig Ball! (Jan. 14, 2026), Doug Austin wrote “the guide is all practical, as in practical applications of the models for litigation and trial practice, followed by ten examples with prompts, ten tips for improved prompts, guidance for understanding limitations and ethical guardrails, and final thoughts for trial lawyers. Craig finishes the guide with an Appendix on an AI prompt to critique and improve keyword searches (an adaptation of one of his past blog posts).”

I have little to add.  But, I’ll try.

Craig wrote: “Used prudently, [AI] functions as the world’s fastest junior associate—minus the hourly rate and parking validation.”  For example:  “One of the simplest and most effective ways to improve your prompts is by asking an LLM to do it for you.  I’ll often give ChatGPT a prompt and then ask it to streamline and improve my prompt before I run it.”  He adds that attorneys must “remain firmly in the driver’s seat.”

I can’t summarize all of Craig’s prompting guidance. But there are many very interesting suggestions.  For examples of prompt design:

• “Use only the materials I provide; do not rely on outside knowledge.”
• “If the record is silent or ambiguous, say so.”
• “End with a list of assertions that require human verification.”

Craig explains key terms, such as “tokens” and “context windows.”  He points out upload issues with large files, how to “chunk” uploads, and he discusses when “older material is displaced” in long sessions.

Two of my favorite use case suggestions are:

“Search Term Generation and Query Assessment

Turning plain-English descriptions into search terms or Boolean queries for discovery is another area where LLMs perform well. They can analyze relevant documents and propose keyword lists, proximity connectors, and alternative formulations based on how people actually communicate rather than how lawyers guess they might. LLMs can also critique existing search terms, identifying overbreadth, redundancy, or likely blind spots. This is useful both for improving your own searches and for evaluating the adequacy of an opponent’s proposed terms.”

“Meet-and-Confer and Proportionality Advocacy

AI tools can assist in drafting meet-and-confer correspondence that explains discovery positions in plain English: why certain custodians matter, why others do not, and how proposed limits reflect proportionality. This is especially useful when translating technical search methodology into language suitable for opposing counsel—or the court. Used carefully, LLMs help sharpen the explanation of discovery positions, not the positions themselves. They are best employed to clarify rationale, not to invent it.”

In that regard, Craig provides the following:

Prompt Example: “Draft a meet-and-confer letter proposing a proportional ESI protocol for a federal employment discrimination case in Texas. Address data sources, custodians, date ranges, search methodology (with examples), metadata fields, production format, and privilege clawback. Explain the rationale for each choice in plain English.”

Tip: you might try uploading my free primer on ESI Protocols along with your prompt:  http://www.craigball.com/ESIProtocol.pdf

However, these limited examples are only a very small sampling of use cases that Craig covers.

Craig cautions: “Finally, remember that LLMs reason statistically, not legally. They recognize patterns in language, not truth. When the record is thin, ambiguous, or contested, the risk of confident but wrong output increases. The antidote is disciplined prompting, scoped inputs, and explicit verification requirements—habits already familiar to good trial lawyers.”  His closing sentence is: “Just remember: every tool needs a craftsman, and no amount of technology can replace the judgment and insight of a seasoned trial lawyer.”

This the fourth in a series of reviews of books on artificial intelligence. Click on the “Artificial Intelligence” TAG, above, for other reviews.