Tag: java
All the articles with the tag "java".
-
Improving RAG quality — reranking and hybrid search
Vector search retrieves semantically similar chunks, but similarity alone doesn't guarantee relevance. Reranking scores retrieved candidates by true relevance. Hybrid search adds keyword matching to catch exact terms. Together they meaningfully improve RAG answer quality.
-
Building a document Q&A chatbot with Spring AI and RAG
This post assembles all of Module 3 and 4 into one working application — ingestion pipeline, pgvector storage, QuestionAnswerAdvisor, streaming responses, and a simple chat interface. By the end, you have a chatbot that answers from your own documents.
-
Chunking strategy in RAG — the decision that silently kills answer quality
How you split documents before indexing determines whether your RAG pipeline retrieves useful context or useless fragments. Chunk too large and embeddings average out. Chunk too small and context is missing. This post covers the tradeoffs.
-
Building your first RAG pipeline with Spring AI
Spring AI's QuestionAnswerAdvisor wires retrieval directly into ChatClient. Attach it to your VectorStore and every call automatically retrieves relevant context before the LLM sees the question. This post builds the complete pipeline.
-
What is RAG and why your AI app almost certainly needs it
LLMs know a lot, but they don't know about your business. RAG — Retrieval-Augmented Generation — fixes this by retrieving relevant documents at query time and injecting them into the prompt. Here is why it exists and when to use it.