Reduced RAG: Stop Stuffing Context Windows and Start Extracting Signals

If you're brand new to RAG, start with RAG Explained and RAG Architecture. This post is for the point where you've built a RAG pipeline that mostly works… and now you're paying for it in cost, fragility, and "why did it answer that?" debugging sessions. What…

DocSummarizer Part 5 - lucidRAG: Multi-Document RAG Web Application

This is Part 5 of the DocSummarizer series, and it's also the culmination of the GraphRAG series and Semantic Search series. We're combining everything into a deployable web application. 🚨🚨 PREVIEW ARTICLE 🚨🚨 Still working out some kinks and adding…

DocSummarizer Part 4 - Building RAG Pipelines

NuGet npm .NET Node.js This is Part 4 of the DocSummarizer series. See Part 1 for the architecture, Part 2 for the CLI tool, or Part 3 for the deep dive on embeddings. The hard part of RAG isn't the LLM. It's everything before the LLM. You've probably seen the…

GraphRAG: Why Vector Search Breaks Down at the Corpus Level

Your RAG system is great at "needle" questions: retrieve a few relevant chunks and synthesise an answer. It struggles with two common query types: Sensemaking: "What are the main themes across this corpus?" Connective: "How does X relate to Y across different…

RAG for Implementers: CPU-Friendly Semantic Search with ONNX and Qdrant

📖 Part of the RAG Series: This is Part 4a - core implementation: Part 1: RAG Origins and Fundamentals - What embeddings are, why they matter Part 2: RAG Architecture and Internals - Chunking, tokenisation, vector databases Part 3: RAG in Practice - Building…

RAG for Implementers: Semantic Search in Action

📖 Part of the RAG Series: This is Part 4b - search features and UI: Part 1: RAG Origins and Fundamentals - What embeddings are, why they matter Part 2: RAG Architecture and Internals - Chunking, tokenisation, vector databases Part 3: RAG in Practice -…

Self-Hosted Vector Databases with Qdrant: A Deep Dive

📖 Related to the RAG Series: This article provides a deep dive into Qdrant, the vector database used in: Part 4: ONNX & Qdrant Implementation - Building semantic search Part 5: Hybrid Search & Auto-Indexing - Production integration Qdrant (pronounced…

RAG for Implementers: Hybrid Search and Automatic Indexing

📖 Part of the RAG Series: This is Part 5 - production integration patterns: Part 1: RAG Origins and Fundamentals - What embeddings are, why they matter Part 2: RAG Architecture and Internals - Chunking, tokenisation, vector databases Part 3: RAG in Practice -…

RAG in Practice: Building Real-World Applications

In Part 1 of this RAG series, we covered the fundamentals of Retrieval-Augmented Generation—what it is, how it works, and the underlying technology (embeddings, vector databases, LLM internals). Now it's time to put that knowledge into practice. This article…

RAG in Practice: Building Real-World Applications

In Part 1 and Part 2 of this series, we covered RAG's origins, fundamentals, and technical architecture. You understand what RAG is, why it matters, and how it works under the hood. Now it's time to put that knowledge into practice. This article shows you how…