RAGRAG from A to Z: retrieve documents then answer
Build a complete RAG pipeline with Spring AI: chunk documents, create embeddings, vector search, augment prompts with sources, and test each step.
LLMs Need Evidence to Answer Correctly: RAG Explained with Examples
LLMs don't inherently know your company's private data. This article explains RAG mechanics, why retrieval determines answer quality, with working Java examples.
Why exhaustive search costs so much?: detailed explanation with examples (RAG)
Flat search in RAG runs in O(n) and is perfectly accurate, but expensive on large data. This article explains IVF, nprobe, and the recall/speed tradeoff through Java examples.
Embeddings for Semantic Search: Detailed Guide with Examples
Learn how embeddings turn text into vectors for semantic matching, applying RAG with a working Java example.
From Found Passages to Grounded Answers in RAG
Finding relevant passages doesn't mean you have an answer. This guide covers rerank pipelines, hybrid search, and grounding so LLMs can answer with evidence and gracefully refuse when data is insufficient.