This blog post demonstrates practical implementations of Prompt Engineering techniques using Spring AI. The examples and patterns in this article are based on the comprehensive Prompt Engineering Guide that covers the theory, principles, and patterns of effective prompt engineering. The blog shows how to translate those concepts into working Java code using Spring AI's fluent ChatClient API. The configuration section outlines how to set up and tune your Large Language Model (LLM) with Spring AI, including selecting the right LLM provider for your use case and configuring important generation parameters that control the quality, style, and format of model outputs. The blog also covers LLM output configuration, including temperature, output length, sampling controls, and structured response format. The blog then demonstrates various prompt engineering techniques, including zero-shot prompting, one-shot and few-shot prompting, and system, contextual, and role prompting. Zero-shot prompting involves asking an AI to perform a task without providing any examples, while one-shot and few-shot prompting provide the model with one or more examples to help guide its responses. System prompting sets the overall context and purpose for the language model, defining the "big picture" of what the model should be doing. The blog provides examples of how to implement these techniques in production Java applications using Spring AI's ChatClient API. The examples in the blog are structured to follow the same patterns and techniques outlined in the original guide, and the demo source code used in this article is available on GitHub. The blog also provides references to relevant research papers and documentation for further reading. Overall, this blog post provides a comprehensive guide to implementing prompt engineering techniques using Spring AI, and is a valuable resource for developers looking to improve the performance and effectiveness of their language models.