AI

Hands-on guides to LLMs, agents, prompt engineering, and the AI tools I run every day for real work, not demos.

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Setup a Private Local RAG Knowledge Base

To build a private Retrieval-Augmented Generation (RAG) system, pair a local vector database like Qdrant with an embedding model like BGE-M3 . Add a local LLM through Ollama , and you can index hundreds of documents and ask questions about them. Your data stays on your machine.

Why RAG? The Problem With Pure LLM Memory

Large language models sound smart, but they are poor knowledge stores. They learn from old training data and know nothing about files you created later or keep private. Ask about your own data, and the model will often guess. Even strong open weight models like Llama 4.0 can invent plausible but wrong answers about content they never saw. For a deeper breakdown of why LLM hallucinations happen and how to measure them, the issue goes beyond missing context.

Building Multi-Step AI Agents with LangGraph

AI agents built on LangGraph run as stateful graphs, not linear prompts. The graph can loop, branch on tool output, retry after a failure, and save its progress. That structure is what lets one agent handle long, multi-step tasks reliably.

Key Takeaways

LangGraph models an agent as a stateful graph, so it can loop, retry, and recover.
The state schema you design up front decides how stable the agent turns out.
Built-in checkpointing lets an agent crash, pause for approval, and resume without lost work.
Conditional edges turn failures into retries instead of dead ends.
One agent task can fire dozens of LLM calls, so plan for cost before you deploy.

Prerequisites

You should know Python 3.11+ and the LangChain basics: LLMs, tools, prompts. The code below uses these versions:

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Run Llama 4 Scout Locally: 24GB VRAM, GGUF, Real Speeds

You can run Llama 4 Scout on a 24 GB consumer GPU, but only with an aggressive quantization and some patience. Scout is a 109B-parameter Mixture-of-Experts model, and even its smallest Unsloth dynamic GGUF build is about 32 GB, so a 24 GB card runs it with CPU offload at roughly 20 tokens per second. This guide covers which Llama 4 model fits your hardware, the real VRAM math, and the fastest way to get it running.