Llm

In previous articles, we integrated local LLMs for test generation and set up semantic search with Qdrant. Now it’s time to tie these ideas together and build a proper RAG application. We’ll create a service that can retrieve relevant notes, pass them into an LLM context, and — crucially — use Tool Calling to perform exact calculations. Using monthly expense tracking as our example, we’ll see how Spring AI 2.0 and Spring Boot 4.1 help unify search, generation, and external tools into a cohesive, reliable solution. ...

Maintaining high code coverage is essential but often tedious — especially when you need to cover controllers, services, repositories, and edge cases individually. AI‑powered test generation can automate most of this work, cutting hours of manual effort (in some cases, up to 40–50% of testing time). However, many enterprises — especially in finance, healthcare, or regulated industries — are not willing to share their codebase with third‑party LLM providers like OpenAI or Anthropic. Security policies, intellectual property concerns, and compliance requirements (e.g., GDPR, SOC2) often mandate that no data leaves the corporate network. ...