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Enhancing LLM Responses with Prompt Stuffing in Spring Boot AI

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Enhancing LLM Responses with Prompt Stuffing in Spring Boot AI Large Language Models (LLMs) like OpenAI's GPT series are incredibly powerful, but they sometimes need a little help to provide the most accurate or context-specific answers. One common challenge is their knowledge cut-off date or their lack of access to your private, domain-specific data. This is where "prompt stuffing" (a basic form of Retrieval Augmented Generation or RAG) comes into play. In this post, we'll explore how you can use Spring Boot with Spring AI to "stuff" relevant context into your prompts, guiding the LLM to generate more informed and precise responses. We'll use a practical example involving fetching information about a hypothetical IPL 2025 schedule. What is Prompt Stuffing? Prompt stuffing, in simple terms, means providing the LLM with relevant information or context directly within the prompt you send i...
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Curious case of MissingMethodInvocationException and Mockito.when

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Android: Starting Wifi scan and using ScanResult

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