spring ai
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Enterprise Java
Prompt Caching in Spring AI with Anthropic Claude
Prompt caching lets Claude reuse the processing it already did on a prompt prefix instead of re-reading it from scratch…
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Enterprise Java
MCP Logging in Spring AI
The Model Context Protocol (MCP) lets an AI application call out to external tools and data sources through a standard…
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Enterprise Java
A Guide to Structured Output in Spring AI
Large Language Models (LLMs) naturally respond with free-form text. That’s great for a chat interface, but most applications need to…
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Enterprise Java
Exploring the Agent2Agent (A2A) Protocol with Spring AI
AI applications are rapidly moving from single-model applications toward systems composed of multiple specialized agents. One agent may specialize in…
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Enterprise Java
Google GenAI Chat with Spring AI
Generative Artificial Intelligence has changed the way developers build intelligent applications. Modern applications can use Large Language Models (LLMs) to…
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Enterprise Java
Spring AI RAG Tutorial with Spring Boot
Retrieval-Augmented Generation, commonly known as RAG, is one of the most useful patterns for building AI applications that need to…
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Enterprise Java
LLM Tool Call Reasoning with Embabel AI
Large language models become significantly more useful when they can interact with external tools instead of only generating text. A…
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Enterprise Java
LLM-as-a-Judge with Spring AI Recursive Advisors
Large Language Models (LLMs) are increasingly used not only to generate content, but also to evaluate the output produced by…
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Core Java
Java as MCP Infrastructure: What Happens When Every Enterprise JVM Becomes an Agent Host
Spring AI, LangChain4j, Quarkus, and Helidon all now speak the Model Context Protocol. That is a bigger shift than another…
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