How do we check that generated prose contains the right information, rather than merely sounding like a good answer? Ask an agent for a refund status when the authoritative tool says pending. “Your refund is complete” answers the question but contradicts the…
lucidRESUME needs to turn one complete career transcript into distinct, credible résumés. Select an applied-AI job: foreground agent architecture, evaluation and tool controls. Select a database job: foreground queries and performance. An early version found…
I asked lucidRESUME for an Engineering Lead résumé. It produced two pages covering six relevant roles, two recent projects and the skills which mattered to the target. The prose was the prose I had already reviewed. Twenty-eight inline links connected the…
A résumé should work like a scientific paper: human-written prose for communication, machine-readable structure for discovery, and references that connect claims to evidence. As I once more hunt for my next paying gig (LinkedIn), I again face my nemesis:…
Deep Research without Deep Pockets. When "Deep Research" landed in the premium AI tools, I was curious: what is this, actually? So I pulled it apart. And once I understood the architecture, I realised I'd already built most of it. DoomSummarizer leverages a…
I built StyloFlow because I kept writing the same pattern over and over: components that react to what happened before, emit confidence scores, and sometimes need to escalate to more expensive analysis. Existing workflow engines wanted me to think in terms of…
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…
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…
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…
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…