Improving the accuracy of domain specific tasks with LLM distillation

LLM distillation is a key method for adapting large language models to real-world tasks. In this session, Shane Johnson presents a scalable approach for improving accuracy in domain-specific applications by distilling large, general-purpose models into smaller, efficient ones. We cover how LLM distillation supports low-latency, cost-effective inference while retaining high task performance. The webinar highlights techniques like weak supervision, labeling functions, and limited-data fine-tuning that accelerate deployment and model adaptation. You’ll learn how LLM distillation fits into enterprise workflows, helping teams move from foundation models to production-ready solutions. This video is ideal for AI teams focused on deploying specialized models that balance accuracy, speed, and resource efficiency. 00:00:01 - Intro & Webinar Overview 00:03:19 - The Cost Challenge of LLMs 00:06:17 - Complexity in Real-World AI Tasks 00:09:34 - Evaluating Domain-Specific Accuracy 00:12:41 - Ranking and Response Generation 00:15:58 - Using DistilBERT and Model Combinations 00:19:25 - Tools for Efficient LLM Distillation 00:22:45 - Architecture of Distilled Models 00:26:11 - Presentation Format and Flow 00:29:34 - Use of Labeled and Weak Data 00:32:57 - Tradeoffs in Deploying Large Models 00:36:15 - Case Study: Enterprise Deployment 00:39:28 - Generalization vs Specialization 00:42:52 - Model Size and Performance Comparison 00:46:20 - Final Thoughts & Q&A