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Prompt Engineering Techniques with Spring AI
- This blog post demonstrates practical implementations of Prompt Engineering techniques using Spring AI.
- Examples and patterns are based on the comprehensive Prompt Engineering Guide covering theory, principles, and patterns of effective prompt engineering.
- The article showcases how to translate concepts into Java code using Spring AI's ChatClient API following specified patterns.
- Configuration section covers setting up and tuning Large Language Models (LLM) with Spring AI.
- LLM Provider Selection enables choosing models like OpenAI, Anthropic, Google Vertex AI, etc., with easy configuration.
- LLM Output Configuration options include temperature, maxTokens, and sampling controls for controlling model responses.
- Structured Response Format allows mapping LLM responses to Java objects directly using Spring AI's methods.
- Model-Specific Options exist for different providers, offering unique features and configurations while maintaining a common interface.
- Prompt Engineering Techniques section covers Zero-Shot, One-Shot & Few-Shot Prompting, System, Contextual, Role Prompting, Step-Back Prompting, Chain of Thought, Self-Consistency, Tree of Thoughts, Automatic Prompt Engineering, and Code Prompting.
- Spring AI and its Java API facilitate the implementation of prompt engineering techniques for building AI applications.
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