RAG vs MCP is the wrong debate — here's the right framing for production AI systems
The question I keep seeing in every AI engineering forum right now: "Should we use RAG or MCP?" It's the wrong question. And the fact that it's being …
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A Unified View of AI Evolution: From Machine Learning to LLMs, RAG, and Fine-Tuning
Artificial Intelligence has progressed far beyond its early rule-based origins. What once depended on predefined logic has evolved into systems that c…
Postgres With pgvector vs Pinecone: 1 Million Embeddings, One Honest Comparison
Book: Database Playbook: Choosing the Right Store for Every System You Build Also by me: RAG Pocket Guide: Retrieval, Chunking, and Reranking Patterns…
Part 4: Improving Retrieval Quality with Token-Aware Chunking and HyDE
Making RAG Smarter with Token-Aware Chunking, HyDE, and Context-Aware Search In Part 3, we improved chunking and optimized context. The system was fas…
Introduction to RAG for LLMs: Sparse (Lexical) RAG and Dense RAG (Semantic Vector Search)
Introduction LLMs store information within their own parameters. By being trained on massive datasets, the models learn this data. But what if they ar…
Plexus: A WiFi Graph RAG for Network Troubleshooting
WiFi troubleshooting has a confidence problem. Ask a chatbot what's causing client disconnections and it'll give you an answer that sounds right. But …
5 Ways Azure AI Search is Revolutionizing Enterprise RAG Architectures
In the rapidly evolving landscape of Generative AI, the transition from experimental Proof of Concepts (POCs) to production-grade applications is the …
О нюансах работы приложений на базе искусственного интеллекта
Когда разработчик получает задачу, он её сначала декомпозирует: разбивает на части, ищет зависимости, проектирует решение. Потом уже пишет код. LLM де…
Stop Losing Your Health Data! Build a Lifelong Electronic Health Record (EHR) System with Neo4j and GraphRAG 🏥💻
Let’s be honest: our medical history is usually a chaotic mess of scattered PDFs, blurry smartphone photos of prescriptions, and "I think I had a feve…
FLAMEHAVEN FileSearch: Why This RAG Engine Feels Different from the Usual Stack
FLAMEHAVEN FileSearch: Why This RAG Engine Feels Different from the Usual Stack RAG is no longer an exotic idea. At this point, most developers have s…
The Hidden Compliance Gap in Every Enterprise RAG Pipeline
Every week, another enterprise announces a RAG-powered AI assistant. Legal teams get a contract review bot. Hospitals get a clinical decision support …
RAG in Practice — Part 5: Build a RAG System in Practice
Part 5 of 8 — RAG Article Series ← Part 4: Chunking, Retrieval, and the Decisions That Break RAG · Part 6 (publishing soon) Why This Article Is Differ…
What is RAG? A Beginner's Guide to Retrieval-Augmented Generation (With a Full Pipeline Walkthrough)
If you've ever wondered how ChatGPT-style apps can suddenly "know" about your company's internal documents, product manuals, or legal files without be…
LLM vs RAG
LLM (Large Language Model) An LLM like GPT-4 or Claude is: A pretrained model on massive text data Generates answers based on what it has learned duri…
Browser-Based LLMs in Healthcare
The most persistent tension in healthcare AI isn't about model capability — it's about data. Sending a patient's protected health information (PHI) to…
Build Your Own Second Brain: RAG-Powered Knowledge Tools That Never Leave Your Machine
Tags: #ai #python #rag #productivity Every day, we generate an enormous volume of personal knowledge — research papers we read, journal entries we wri…
RAG vs GraphRAG: When to Use What (From a Builder’s Perspective)
I wasted time overengineering a GraphRAG system… when a simple RAG pipeline would’ve done the job better. If you’re building with LLMs, you’ll hit thi…
I built a production RAG pipeline. Here's what most tutorials skip.
I wanted a RAG system that was fast to run and fast to set up for clients. Upload a PDF, ask questions, get answers with citations. Pretty standard st…
Week 5: RAG Systems and AI Agents - Where Distributed Systems Meet LLMs
Week 5 done. This week: RAG systems and AI agents - making LLMs actually useful with real data. This week was about building systems around LLMs, not …
The “The Architecture Handbook for Milvus Vector Database” Book Review
A feedback and my thoughts on the “The Architecture Handbook for Milvus Vector Database” book. The image from the book cover produced by Packt Publica…
Your RAG Chatbot Lies Because You're Chunking Wrong
Three weeks into building FolioChat — a chatbot that lets portfolio visitors talk to my GitHub — I had a system that gave answers like a drunk Wikiped…
What are Pre-Trained Models, Fine-Tuning, RAG, and Prompt Engineering? A Simple Kitchen Guide
Explained Using Food The Analogy That Finally Makes It Click Introduction I’ve been asked the same question a thousand times. It comes from senior eng…
Why Chunking Is the Biggest Mistake in RAG Systems
Retrieval-Augmented Generation (RAG) has become the default architecture for building AI-powered document intelligence systems. Most implementations f…
I Built a Self-Improving RAG System for Claude Code. Here is What It Learned.
Originally published at chudi.dev I was debugging the same authentication error for the third time this month. Same error. Same root cause. Same fix. …
EidolonDB – Self-managing memory for AI agents
I got tired of my agents making things up in long-horizon or multi-session workflows. So I built a memory layer that refuses to. EidolonDB gives agent…