Iryna Dohndorf
2,355 viewsTiberius: A Security Testing Framework for LLM Applications in Java
Tiberius: A Security Testing Framework for LLM Applications in Java How do you write a regression test for a system that is …
Category
LangChain4j
Iryna Dohndorf
2,355 views
Miro Wengner
8,249 viewsJC-AI Newsletter #15
Over the past two weeks, the field of artificial intelligence has continued its remarkable pace of advancement. As AI becomes increasingly …
Buhake Sindi
3,985 viewsBring AI into your Jakarta EE apps with LangChain4J-CDI
Goal: This article will demonstrate how to add AI features to a Jakarta EE / MicroProfile application using LangChain4J‑CDI, with simple to …
Miro Wengner
9,757 viewsJC-AI Newsletter #6
Fourteen days have passed, and it is time to present a fresh collection of readings that could influence developments in the field of …
Miro Wengner
6,943 viewsJC-AI Newsletter #4
14 days have passed and it's time for a new batch of readings that could shape developments in the field of artificial intelligence.
A N M Bazlur Rahman
4,028 viewsAI Applications with LangChain4j Guardrails and Spring Boot
Learn how to build secure AI applications using LangChain4j guardrails in Spring Boot. Implement input/output validation, prevent prompt …
Frank Delporte
12,105 viewsFoojay Podcast #74: JCON Report, Part 3 – AI, ChatGPT, LLM, ML, RAG, MCP, GenAI, and more
Let's have an AI Bingo and talk about ChatGPT, LLM, ML, RAG, MCP, GenAI, and more! This is part 3 of the interviews recorded at the JCON …
Markus Eisele
2,785 viewsSentiment Analysis with Java, Quarkus, LangChain4j, and Local LLMs
Let’s get in and build a Quarkus REST API that classifies text sentiment using a local LLM model (like Phi-3 Mini) pulled in via Ollama.
Brian Vermeer
3,436 viewsEnsuring Safe and Reliable AI Interactions with LLM Guardrails
We know that LLMs can and will make mistakes, and while enriching your prompts with the proper context can help align results with your …
Brian Vermeer
3,993 viewsWhat is RAG, and How to Secure It
The implementation of RAG introduces security considerations. Risks such as prompt injection, data poisoning, access control gaps, and data …