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AI

  • in AI
  • 9 min read

You ask an AI assistant a question. It confidently gives you an answer — but the answer is wrong, outdated, or completely made up. This is called a hallucination, and it's one of the most frustrating problems with large language models (LLMs) out of the box.

RAG (Retrieval-Augmented Generation) was invented to fix exactly this. And Agentic RAG takes that fix to a whole new level. In this guide, we'll break down both architectures from scratch — what they are, how they work step by step, and when to use which.

  • in AI
  • 18 min read

vLLM: Production LLM Serving from Zero to Scale

You've downloaded a large language model. You've got it running. But you notice something uncomfortable: it's slow, it can only handle one request at a time, and your GPU is mysteriously underutilized. The moment two people try to use your model at the same time, one of them waits — and waits.

This is the LLM serving problem, and vLLM is the most widely adopted open-source solution to it.