Engineering AI Outcomes: Content, Pathways, and Quality
When AI output falls short, rewording the prompt is rarely the fix. This course gives attorneys a systematic approach to the three things that actually determine output quality.
Course curriculum
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Course Overview
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Building Context for More Consistent and Robust AI Outputs
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Your Turn: Diagnose a Weak AI Output
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How to Structure Better AI Outputs
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Your Turn: Build a Content Block for a Current Matter
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Training Your AI to "Think" Like a Lawyer
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Your Turn: Map a Multi-Step Task onto a Structured Pathway
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How to Reduce Errors and Produce More Predictable and Reliable Outputs
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Your Turn: Build Your First Framework Prompt
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About this course
- Free
- 9 lessons
- 1 hour of video content
Instructor
Alex Shahrestani
Founding Partner, Attorney, and Software Developer - Promise Legal
Alex Shahrestani is Managing Partner of Promise Legal, an AI-native technology law firm in Austin, Texas. He came to law from engineering: fifteen years writing software and seven years building AI and machine learning systems, most of it well before the current wave. Today he runs a practice serving more than 500 startups — including graduates of Y Combinator, Techstars, and Capital Factory — on infrastructure he architects and maintains himself. That is what he brings to a classroom of lawyers. He is not forecasting what AI might do inside a law practice; he is reporting what it already does inside his.
Alex has been selected to Super Lawyers' Texas Rising Stars every year since 2021 and named to the ABA's On The Rise — Top 40 Young Lawyers. He serves on the State Bar of Texas Computer & Technology Section's Emerging Technology Committee and is Vice President of EFF-Austin, an independent digital civil liberties nonprofit. He speaks regularly at ABA TECHSHOW, SXSW, and State Bar programs on AI governance and legal technology.