Building a "Lawyer GPT" for Your Blog - Part 9: Document Ingestion with Docling

Welcome to Part 9! In previous parts, we've built a robust RAG system that processes markdown blog posts and makes them searchable through semantic embeddings. Now it's time to expand our capabilities with Docling - a powerful document processing library that…

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…

RAG Architecture and Internals: How It Really Works

In Part 1, we covered RAG's origins, fundamentals, and why it matters. You understand the high-level concept: retrieve relevant information, then use it to generate responses. Now we dive deep into the technical architecture—exactly how RAG systems work under…

RAG Explained: Origins and Fundamentals

Ever searched for "deployment guide" and got nothing, even though there's an article about "publishing to production"? RAG (Retrieval-Augmented Generation) solves this by understanding meaning, not just keywords. This series shows you how RAG came about, how…

Cooking with DiSE (Part 3): Untrustworthy Gods - When Your LLM Might Be Lying to You

In which we discover that your friendly AI assistant might have ulterior motives (and what to do about it) Note: This is Part 3 in the "Cooking with DiSE" series. If you haven't read Parts 1-2, you might want to—though this one stands alone as a slightly…