This case study examines how Casetext, a legal technology company, successfully deployed GPT-4 in production to create Co-Counsel, an AI legal assistant that fundamentally transformed legal work. The story represents a significant milestone in LLMOps, showing how careful testing, evaluation, and deployment strategies can make LLMs reliable enough for mission-critical professional applications. The company's journey started with early access to GPT-4 through their relationship with OpenAI. After seeing GPT-4's capabilities, CEO Jake Heller and the leadership made the bold decision to pivot their entire 120-person company to focus on building Co-Counsel in just 48 hours. This rapid transition was driven by the dramatic improvement in capabilities from previous models - while GPT-3.5 scored in the 10th percentile on bar exams, GPT-4 scored in the 90th percentile. From an LLMOps perspective, several key technical and operational approaches were crucial to their success: Test-Driven Development Approach: * They implemented a comprehensive test-driven development framework for prompt engineering * Started with dozens of tests, eventually scaling to thousands * Each prompt and capability had clear "gold standard" answers to validate against * This approach was crucial for maintaining reliability and catching edge cases * Tests helped identify patterns in model mistakes that could then be addressed through improved prompting Skills-Based Architecture: * They broke down complex legal tasks into smaller "skills" * Each skill represented a discrete capability (like document analysis or search query generation) * Skills were chained together to handle complex workflows * This modular approach allowed for better testing and reliability Prompt Engineering Strategy: * Developed detailed prompts that guided the model through step-by-step reasoning * Created specific instructions for handling different document formats and legal contexts * Built prompts that could handle edge cases like handwritten annotations and unusual document layouts * Focused on getting 100% accuracy rather than accepting "good enough" results Integration with Legal Infrastructure: * Built connections to specialized legal document management systems * Developed custom OCR processing for legal documents * Created integrations with legal research databases * Handled complex document formatting specific to legal documents (like 4-up printing layouts) Quality Assurance and Validation: * Implemented extensive testing with real legal documents and queries * Created verification systems to ensure citations and quotes were accurate * Built safeguards against hallucination and incorrect information * Focused on gaining lawyer trust through consistent accuracy Deployment Strategy: * Initially deployed under NDA to select customers before public release * Gathered extensive feedback from early users * Rapidly iterated based on real-world usage * Maintained tight control over feature release and quality The results were remarkable in terms of both technical achievement and business impact. Co-Counsel could complete tasks in minutes that previously took lawyers full days, such as reviewing massive document sets for evidence of fraud or conducting comprehensive legal research. The system's accuracy and reliability gained the trust of traditionally conservative legal professionals. A particularly noteworthy aspect of their LLMOps approach was their focus on achieving 100% accuracy rather than accepting partial success. This was driven by their understanding that in legal work, even small errors could have significant consequences. They found that if they could get a prompt to pass 100 test cases, it would likely maintain that level of accuracy across hundreds of thousands of real-world inputs. The team's experience also yielded important insights about LLM deployment in professional contexts: * The importance of first impressions - early interactions needed to be flawless to gain user trust * The value of domain expertise in prompt engineering * The need for extensive testing and validation in professional applications * The importance of handling edge cases and document formatting challenges The success of their LLMOps approach was validated by the market - within two months of launching Co-Counsel, Casetext entered acquisition talks with Thomson Reuters, ultimately leading to a $650 million acquisition. This represented a massive increase from their previous $100 million valuation and demonstrated the value of their systematic approach to deploying LLMs in production. Their experience with newer models like GPT-4 and Claude 2 continues to show the importance of rigorous testing and evaluation. With Claude 2, they've found it can handle more nuanced tasks like identifying subtle misquotations in legal briefs, demonstrating how LLM capabilities continue to evolve and the importance of maintaining robust testing and deployment practices. This case study demonstrates that while LLMs may start with capability limitations or accuracy challenges, a systematic LLMOps approach focusing on testing, prompt engineering, and careful deployment can create highly reliable AI systems suitable for even the most demanding professional applications.