Uploaded August 2025 | Updated September 2026, 2 weeks ago
Jake Heller has spent years building AI tools for lawyers. With early access to GPT-4, he and his team realized the model could finally perform legal work at a professional level—scoring in the 90th percentile on the bar exam where GPT-3.5 had only reached the 10th. That breakthrough led to Co-Counsel, an AI legal assistant for research and contracts, and eventually to Casetext’s acquisition by Thomson Reuters.
In this video, Jake breaks down what it takes to turn powerful models into reliable products, and the lessons he’s learned from building AI for one of the world’s most demanding professions.
Chapters:
00:28 - Early Work with GPT-4
00:53 - Pivot to Co-Counsel
01:38 - Success with GPT-4
02:34 - Acquisition by Thomson Reuters
02:57 - Introduction to Context Engineering
03:24 - Developing Co-Counsel: Three Big Steps
03:44 - Defining the Customer Experience
04:57 - Legal Research Example
06:13 - Linear vs. Agentic Tasks
08:02 - Writing Effective Prompts
12:44 - Importance of Context
13:33 - Challenges in Prompt Engineering
15:49 - Tricks and Tips for Prompt Engineering
18:18 - Reinforcement Fine-Tuning and Model Selection
Jake Heller has spent years building AI tools for lawyers. With early access to GPT-4, he and his team realized the model could finally perform legal work at a professional level—scoring in the 90th percentile on the bar exam where GPT-3.5 had only reached the 10th. That breakthrough led to Co-Counsel, an AI legal assistant for research and contracts, and eventually to Casetext’s acquisition by Thomson Reuters.
In this video, Jake breaks down what it takes to turn powerful models into reliable products, and the lessons he’s learned from building AI for one of the world’s most demanding professions.
Chapters:
00:28 - Early Work with GPT-4
00:53 - Pivot to Co-Counsel
01:38 - Success with GPT-4
02:34 - Acquisition by Thomson Reuters
02:57 - Introduction to Context Engineering
03:24 - Developing Co-Counsel: Three Big Steps
03:44 - Defining the Customer Experience
04:57 - Legal Research Example
06:13 - Linear vs. Agentic Tasks
08:02 - Writing Effective Prompts
12:44 - Importance of Context
13:33 - Challenges in Prompt Engineering
15:49 - Tricks and Tips for Prompt Engineering
18:18 - Reinforcement Fine-Tuning and Model Selection
