Tag: llm

Most RAG demos look great until you ship them. Ragas measures faithfulness, context precision, answer relevancy, the metrics that actually predict user trust.

How tiny 7B and 8B models keep punching above their weight, knowledge distillation, the teacher-student trick that makes local AI actually usable on home hardware.

Open WebUI Tools, Functions, and Pipelines do three different jobs and the names do not help. What each one is, when to use which, and working code for all three.

Self-supervised learning is the technique behind GPT, BERT, and modern LLMs. Learn how models teach themselves from unlabeled data.

GitHub Copilot is great until you read the ToS. Continue.dev, Cody, and Tabby bring AI code assistance to your editor with local or self-hosted models, no code leaves your machine.

Every RAG tutorial says 'just use Chroma.' Then you hit production. Here's what Qdrant, Weaviate, and ChromaDB actually offer and when each one earns its place.

LangChain does everything and LlamaIndex does one thing brilliantly. Here's how to pick the right RAG framework without regretting it at 2 AM.

RAG is the default answer for giving LLMs access to documents. But chunking, embedding, and retrieval introduce failure modes that a virtual filesystem sidesteps entirely.

Google's Gemma 4 is the best open model they've shipped yet. Here's how to pull it, run it, and actually use it for real work with Ollama on your own hardware.

1-bit models store weights as -1, 0, or 1. That sounds insane until you see them run a 100B parameter model on a laptop CPU. Here's what's actually happening.

AMD finally has a fast, open source local LLM server that uses both GPU and NPU. If you've been jealous of Nvidia users, Lemonade is worth your time.

JSON mode forces a model to return valid JSON, which helps extraction and hurts reasoning. When to use it, when it degrades output, and a hybrid approach.