There’s a conversation that keeps happening. An engineer tries to explain what a language model does and falls back on “neural networks,” “tokens,” “parameters.” Across the table, a lawyer nods with the courtesy of someone who stopped understanding two sentences ago. Then each goes back to their office convinced the other doesn’t speak their language. And both are right.

I’ve seen that gap between the technical and legal worlds up close. Few people cross it with ease, and I want to be good at that, because the problem is real: lawyers make decisions about technology they don’t fully understand, and engineers build tools for a legal context they don’t fully grasp either.

When I founded Trifolia, I was clear about what I didn’t want: another company of engineers selling engineering solutions to lawyers, with the Silicon Valley mindset that technology explains itself. That doesn’t work. What we want is to solve legal problems, to start from legal practice and use technology as a means, not an end. And for that, the first step is education: that the lawyer understands what’s behind these tools. Not to become an engineer, but to stop depending blindly on whoever sells them the software.

With that conviction I put together “Technical Foundations of AI for Lawyers”: 20 interactive slides that run from what an LLM is and how it generates text, to hallucinations, context windows, data privacy, and practical considerations for legal work. The material is completely open (CC BY-NC-SA 4.0): educacion.trifolia.cl.

One clarification: these slides are support material for a live presentation. Some aren’t completely self-contained; I’m working on that.

We’ve used it in two workshops run from Trifolia:

  • University of Talca (January 2025): in-person workshop, 65 attendees (video available).
  • Online workshop (February 6, 2025): 100 attendees.

100% of attendees said the content exceeded or met their expectations, and 86.3% felt the level was right. The remaining 13.7% found it too advanced, which tells me we’re treading the right edge.

But satisfaction surveys measure perception, not accuracy. And at this stage what I need most is feedback:

  • Engineers (especially software and ML): Did I oversimplify anything to the point of making it wrong? Making the complex accessible always runs the risk of distorting it.
  • Lawyers: Am I falling into unnecessary jargon? Or, on the contrary, am I underestimating what you already know? The sweet spot is hard to find without your eyes.
  • Law professors: I’d love for this material to help you explain these technologies to your students. If you use it, tell me what works and what doesn’t.

There’s a feedback form in the slides, or you can write to me at [email protected]. The repository accepts issues.

Originally published on LinkedIn on February 11, 2026.