<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AI on Adrien CAUBEL</title><link>https://blog.adriencaubel.fr/series/ai/</link><description>Recent content in AI on Adrien CAUBEL</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 06 Nov 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.adriencaubel.fr/series/ai/index.xml" rel="self" type="application/rss+xml"/><item><title>Create RAG with LangChain4j (no spring required)</title><link>https://blog.adriencaubel.fr/posts/ai/langchain4j/</link><pubDate>Fri, 06 Nov 2026 00:00:00 +0000</pubDate><guid>https://blog.adriencaubel.fr/posts/ai/langchain4j/</guid><description>Tip GitHub project : https://github.com/adrien1212/rag-poc.git LLMs are impressive for general knowledge, but ask one about your internal docs, your contracts or your product catalog and it&amp;rsquo;ll confidently make things up. That&amp;rsquo;s where RAG (Retrieval-Augmented Generation) comes in: instead of hoping the model knows the answer, you fetch the relevant pieces from your own documents and feed them as context. The model then answers based on your data, not its training set.</description></item></channel></rss>