#rag
13 articles tagged rag.
Agent Memory Patterns: How to Give Your LLM Application a Brain That Persists
Your LLM forgets everything the moment the request ends. Here's how production systems build short-term, long-term, and semantic memory — with Spring AI, Redis, and pgvector examples.
Why Most Enterprise AI Projects Die in Production (And the 5 Patterns That Actually Survive)
I've deployed AI systems to 10,000+ enterprise users across healthcare, logistics, and financial services. The projects that failed didn't fail because of the model. They failed because of decisions made before the first line of model code was written.
Vector Database Comparison: pgvector vs Pinecone vs Qdrant
A practical comparison of the leading vector stores for RAG — pgvector, Pinecone, and Qdrant — on scale, filtering, hybrid search, ops burden, and cost, with clear guidance on when to pick each.
The Enterprise AI Data Readiness Checklist
AI succeeds or fails on data. A practical, dimension-by-dimension checklist to assess whether your enterprise data is ready for RAG, agents, and LLM features — before you build.
Building a RAG Chatbot with Spring AI and PGVector
A step-by-step guide to a production-ready RAG chatbot in Java with Spring AI, PostgreSQL, and pgvector — ingestion, embeddings, retrieval, and grounded answers.
Spring AI: From Beginner to Expert — Course Overview
A complete Spring AI course for Java teams: setup, prompting, tool calling, RAG, MCP, choosing an LLM, and production architecture.
Context Engineering: The Real Skill Behind Reliable LLM Apps
What you put in the context window matters more than prompt wording. A practical guide to context engineering — the budget, techniques, and failure modes.
Zero to Production: Building Your First Enterprise LLM Application
A four-phase guide to taking an LLM prototype to a production enterprise app — RAG, caching, observability, cost control, and multi-model routing.
RAG Systems Explained
A complete guide to Retrieval Augmented Generation — how it works, why each component matters, and how to build production-grade RAG pipelines.
Spring AI Enterprise Integration Guide
A practical guide to building enterprise-grade AI applications with Spring AI — from chat clients and RAG pipelines to production observability.
RAG Chunking Strategies That Actually Improve Retrieval
Your RAG quality is capped by how you chunk. A practical comparison of fixed, recursive, semantic, and structural chunking, with sizing and overlap tips.
Evaluating RAG Systems: Metrics That Catch Real Failures
You can't improve a RAG system you can't measure. The metrics that matter — faithfulness, relevance, context precision and recall — and how to build an eval loop.
Fine-tuning vs RAG vs Prompting: How to Choose
Teams reach for fine-tuning when they need RAG, or RAG when a better prompt would do. A decision framework for choosing the right approach by problem type.