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

Abstract

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

Objectives: This 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 methods: Demonstration of interaction techniques based on practical examples with concrete recommendations for their application in routine radiological practice.

Results: Advanced 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.

Conclusions: The 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.