[Optimized interaction with Large Language Models : A practical guide to Prompt Engineering and Retrieval-Augmented Generation].

backgroundGiven the increasing number of radiological examinations, large language models (LLMs) offer promising support in radiology. Optimized interaction is essential to ensure reliable results.

objectivesThis article provides an overview of interaction techniques such as prompt engineering, zero-shot learning, and retrieval-augmented generation (RAG) and gives practical tips for their application in radiology. MATERIALS AND

methodsDemonstration of interaction techniques based on practical examples with concrete recommendations for their application in routine radiological practice.

resultsAdvanced interaction techniques allow task-specific adaptation of LLMs without the need for retraining. The creation of precise prompts and the use of zero-shot and few-shot learning can significantly improve response quality. RAG enables the integration of current and domain-specific information into LLM tools, increasing the accuracy and relevance of the generated content.

conclusionsThe use of prompt engineering, zero-shot and few-shot learning, and RAG can optimize interaction with LLMs in radiology. Through these targeted strategies, radiologists can efficiently integrate general chatbots into routine practice to improve patient care.