New AI Pipeline Builds Domain-Specific LLMs with Guided Data Generation

This research introduces a cost-effective and scalable method to create smaller, specialized Large Language Models (LLMs) for specific domains, addressing the limitations of relying on large models or extensive training data. The approach utilizes guided synthetic data generation combined with domain data curation, enabling efficient model training and deployment. The DiagnosticSLM model, tailored for industrial fault diagnosis, demonstrates the effectiveness of this pipeline.