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.
