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RAG Is Not a Feature: Why Your AI Still Hallucinates
RAG is an engineering system, not a feature. Build production-grade AI by mastering hybrid retrieval, query routing, and evaluatio...
I Built a RAG System for Our Analytics Team. It Worked Great Until We Added Real...
RAG was supposed to help analysts and stakeholders get answers about our data. But the system was wrong in ways that would have co...
From Exact kNN to DiskANN: The Evolution of High-Performance Vector Search
Scaling vector search isn't one problem. It's three. You hit a compute wall first, and you run into a memory wall. And each proble...
I Built a Project-Specific LLM From My Own Codebase
A developer built a local AI assistant to help new engineers understand a complex codebase. Using a Retrieval-Augmented Generation...
Lessons From Designing Production AI Architectures
Production AI success depends more on systems engineering than model quality. Retrieval, latency, observability, guardrails, cost...
RAG: A Data Problem Disguised as AI
RAG systems fail because of broken data pipelines, not bad models. Poor chunking orphans meaning. Stale indexes serve outdated ans...
Do LLMs Really Lie? Why AI Sounds Convincing While Getting Facts Wrong
AI hallucinations aren’t random glitches — they’re a natural consequence of how large language models are trained to predict plaus...
From LLM to Agent: How Memory + Planning Turn a Chatbot Into a Doer
LLM agents aren’t magical upgrades—they’re system designs. By combining models with memory, planning, tool use, and control loops,...
OpenClaw: An AI Lobster That Gets Work Done
OpenClaw is an open‑source AI assistant you run on your own machine. It works inside the chat apps you use and can learn your habi...
LLMs as Integration Endpoints: Building Apache Camel Routes With LangChain4j Cha...
This tutorial shows how to integrate LLM chat into Java applications using Apache Camel and LangChain4j, covering prompt templates...
RAG is a Data Problem Pretending to Be AI
Retrieval-Augmented Generation fails most often not because LLMs “hallucinate,” but because retrieval pipelines return incomplete,...
