DocSummarizer Part 3 - Advanced Concepts: The "I Went Too Far 🤦" Deep Dive

This is Part 3 of the DocSummarizer series: Part 1: Building a Document Summarizer with RAG - The architecture and why the pipeline approach beats naive LLM calls Part 2: Using the Tool - Quick-start guide for the CLI Part 3: Advanced Concepts (this article) -…

DocSummarizer Part 2 - Using the Tool

GitHub release .NET Version This is Part 2 of the DocSummarizer series. See Part 1 for the architecture and patterns, or Part 3 for the deep technical dive into embeddings and retrieval. Turn documents or URLs into evidence-grounded summaries - for humans or…

Stop Shoving Documents Into LLMs: Build a Local Summarizer with Docling + RAG

Here's the mistake everyone makes with document summarization: they extract the text and send as much as fits to an LLM. The LLM does its best with whatever landed in context, structure gets flattened, and the summary gets increasingly generic as documents get…

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…