Fetching and Analysing Web Content with LLMs in C#

📌 Note: This article teaches the fundamentals of web content extraction with LLMs using the simplest possible approach. For production use cases (web summarization, document analysis, agent tools), see DocSummarizer GitHub release - it implements the…

How to Analyse Large CSV Files with Local LLMs in C#

Series: Local LLMs for Data - Part 1 of 2 Here's the mistake everyone makes: they try to feed their CSV into an LLM. Don't. LLMs should generate queries, not consume data. You've got a 500MB CSV file and want to ask "What's the average order value by region?"…

Why I Don't Use LangChain (and What I Do Instead)

I'm a .NET developer. When I started building LLM-powered systems, everyone pointed me toward LangChain. "It's the standard," they said. "All the examples use it." And they were right - if you're in the Python ecosystem, LangChain is everywhere. But here's the…

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…

Building a Frontend Before the API is Ready: Without Brittle Fixtures

How many times have you been blocked waiting for backend APIs to be ready? Or spent hours maintaining brittle mock data that becomes stale the moment requirements change? Enter mostlylucid.mockllmapi - a production-ready ASP.NET Core mocking platform that uses…

Why Most Commercial 'AI' Projects Are Dumb

"We're building an AI-powered knowledge assistant that will revolutionise how our employees access information..." Every job advert, pitch deck, and consultancy proposal in 2024-2025 Introduction I've spent the past month or so properly immersing myself in the…

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…