AutoAdapt: Automated domain adaptation for large language models

At a glance - Problem: Adapting large language models to specialized, high-stakes domains is slow, expensive, and hard to reproduce. - What we built: AutoAdapt automates planning, strategy selection (e.g., RAG vs. fine-tuning), and tuning under real deployment constraints. - How it works: A structured configuration graph maps the full scope of the adaptation process, an agentic planner selects and sequences the right steps, and a budget-aware optimization loop (AutoRefine) refines the process within defined constraints. - Why it matters: The result is faster, automated, more reliable domain adaptation that turns weeks of manual iteration into repeatable pipelines. Deploying large language models (LLMs) in real-world, high-stakes settings is harder than it should be. In high-stakes settings like law, medicine, and cloud incident response, performance and reliability can quickly break down because adapting models to domain-specific requirements is a slow and…