Great conversation Lukas Biewald! So many valuable insights in the interview! Many of the points really resonated with me and I learned a ton from the real-world experience of Jake Heller & the #casetest team who built an innovative & one of the most successful GenAI product in the Legal space - #cocounsel Here are my key takeaways for building successful GenAI projects, based on this discussion and my own experiences. Key points from the interview: 1. Problem selection: Focus on major, real-world customer pain points 2. Advanced model selection: GPT-4 class for complex use cases. (I believe Claude 3.5, or Gemini 1.5 Pro also would be good options.) 3. Task decomposition and effective prompt engineering 4. Comprehensive dataset building, including edge cases 5. Rigorous testing and iterative improvement 6. No need for Fine Tuning (at least for them): Better prompts & extensive testing outperformed fine-tuning less powerful models like GPT 3.5 or Llama 70B. Presumably the cost overhead of Fine Tuning & deploying new models based on GPT-4 model was not worth it in their use case. 7. Collaboration with domain experts at every stage 8. Use of proprietary data (legal case documents), likely using RAG architecture 9. Importance of user-centric design and seamless integration 5. Ethical AI practices & Fair use: Addressing copyrighted data in LLM training, and impact to content creation & content distribution 6. Societal impact: Changes to legal profession, especially for junior roles and education. In the future, the “AI worker/agent” will do many of the tasks at “human level quality at superhuman speeds”, and the humans will hopefully have “more interesting/engaging” work. Additional considerations/challenges from my experience: 12. Data management: Collection, curation, vectorization, and storage 13. Data governance: Ensuring privacy, security, and compliance 14. Scalability and performance optimization: Design for increasing loads 15. Continuous learning: Regular updates to models, prompts, and evaluations 16. Explainability: Crucial for AI decisions in regulated industries 17. Cost management: Balancing model performance with operational costs 18. Cross-functional team building: Blending AI expertise with domain knowledge Building GenAI products is an exciting journey filled with challenges and opportunities. I'm curious to hear about your experiences. What challenges have you faced? Any success stories or lessons learned to share? Let's continue this valuable discussion and push the boundaries of what's possible with Data, AI & GenAI! #GenAI #AIProjects #TechTrends #ProductDevelopment Lukas Biewald Jake Heller #GradientDissent #cocounsel
Tune in tomorrow for the latest episode of #GradientDissent where host Lukas Biewald, is joined by Jake Heller, Head of Product, CoCounsel. They discuss how AI is leading to innovation in legal practices and reshaping educational approaches for aspiring lawyers. Listen 🎧 or watch 🎥 now: https://lnk.to/i9QHak