Law Punx: 'AI Model Fine-Tuning Is Overrated' - Scott Stevenson - SpellbookIn this Law Punx blast, Scott Stevenson of Spellbook, discusses the limitations of fine-tuning legal AI models, arguing that it has become an overrated technique. He emphasizes the importance of using large language models as layers of human reasoning rather than relying on their long-term memory. The discussion also covers the advantages of real-time information retrieval over fine-tuning and the significance of preference learning in legal AI applications.TakeawaysFine-tuning legal AI models is often ineffective.Large language models should be viewed as layers of reasoning.Real-time information retrieval is superior to fine-tuning.Models can hallucinate when relying on long-term memory.Preference learning is crucial for improving AI accuracy.The acceptance rate of AI suggestions is a key metric.RAG (retrieval-augmented generation) is a promising approach.Legal tech tools should focus on application layers.Training models for everyone limits their effectiveness.AI models should fetch information rather than memorize it.