A developer demonstrates how to build a Spring AI application using OpenAI with Spring Boot, covering project setup, configuration, and creating a REST API that sends prompts to an AI model. The tutor…
Rifat Cakir released spring-ai-test-tools, a VCR-style library that records real Spring AI interactions and replays them deterministically in tests, eliminating slow, flaky, and costly live model call…
Spring AI, a framework from the Spring ecosystem, provides Spring-friendly abstractions for integrating AI capabilities such as chat models, embeddings, vector stores, and RAG into Java applications. …
Spring AI's token usage observability, built on Micrometer, lets developers measure cost per feature before optimizing model selection or ChatClient defaults. The first of a four-part series on LLM co…
A developer discovered that maintaining 23 scattered prompt strings across a codebase, including three conflicting versions of a legal-summary prompt, is an engineering problem rather than an AI probl…
A developer demonstrates how Spring Boot AI applications are vulnerable to both prompt injection and system prompt leakage when user input is concatenated into a single prompt turn. The post shows a v…
A developer demonstrates a technique for zero-latency JSON parsing from LLM responses by using Claude's prefill feature with Spring AI and Java 26 Records. By pre-populating the assistant's response w…
A developer has built an asynchronous human-in-the-loop AI agent system using Spring AI that avoids blocking virtual threads during approval workflows. The approach serializes the agent's ReAct loop s…
In 2026, AI agents are performing real-world transactions like booking flights and mutating databases, but standard database transactions fail when dealing with asynchronous LLM API calls. To maintain…
Basic vector search is inadequate for enterprise AI pipelines and promotes GraphRAG, a hybrid retrieval approach combining vector search with graph databases. It describes a Spring AI and Neo4j implem…