Prompt Less, Infer More: Static Analysis-Guided LLM Type Annotation for Python
Type annotations improve the maintainability and reliability of Python programs, but many real-world projects remain under-annotated because manual annotation is time-consuming and error-prone. Recent neural and LLM-based type inference systems can recover missing annotations, yet they often trade accuracy for efficiency. Existing approaches often fail to exploit project-wide type evidence, or do so inefficiently through unnecessary model calls and noisy prompts. We present SALT, an LLM-based type inference system guided by static analysis. SALT integrates static analysis with LLM inference to resolve types statically, prioritize LLM queries, and construct concise type-aware context. To demonstrate the effectiveness of SALT, we evaluated it on 12 real-world Python projects. SALT achieves the best accuracy among the evaluated systems and reduces estimated LLM cost by up to 78.5% compared with a strong coding-assistant baseline. It also shows similar cost savings with the frontier model GPT-5.5, demonstrating that SALT remains effective with strong LLMs.