Enterprise Java
-
How to Switch Between AI Models Automatically
AI applications do not always need the same language model for every request. A simple factual question may only require…
Read More » -
Intelligent Document Processing with Apache Camel, Docling & LangChain4j
Enterprise applications often need to process large numbers of documents such as PDFs, invoices, manuals, reports, and policy documents. Extracting…
Read More » -
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…
Read More » -
Introduction to HyDE: Hypothetical Document Embeddings for RAG
Retrieval-Augmented Generation (RAG) has become one of the most common approaches for building AI applications that need to work with…
Read More » -
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…
Read More » -
Introduction to Apache Causeway
Building an enterprise application often involves implementing the same infrastructure repeatedly: domain objects, user interfaces, validation, persistence, navigation, and business…
Read More » -
LLM Integration: Apache Camel & OpenAI Component
Large Language Models (LLMs) are increasingly becoming part of enterprise applications. However, integrating an LLM into an existing application is…
Read More » -
Testcontainers + Spring Boot 4: The Integration Test Setup Teams Actually Use
Not a getting-started guide. The opinionated patterns teams converge on once the novelty wears off and the suite has to…
Read More » -
Spring AI with Local LLMs Using LM Studio
Large Language Models (LLMs) are commonly accessed through cloud APIs provided by services such as OpenAI, Anthropic, or Google Gemini.…
Read More »
