Practical guides.

Guide

Agents — when an LLM gets hands and memory

An LLM with tool use, a loop, and memory. Lots of marketing, few definitions. Here's the plain version.

Guide

Coding agents — when the model touches the repo

Claude Code, Codex and friends are not magical juniors. They are a fast loop: read code, edit, run tests, repair fallout. Useful, but only with guardrails.

Guide

Context window — how much hell fits in a prompt

A context window is how many tokens a model can see at once. A bigger window is not memory, truth or a guarantee of better answers. It is a larger, pricier workbench.

Guide

Golden Dataset — ground truth for an AI system, not a golden cage

A Golden Dataset is a small, carefully reviewed set of real cases used to tell whether an AI system actually works. In Skillmea AI we use it to evaluate course recommendations against lesson evidence, not marketing blurbs.

Guide

Koog and Kotlin AI agents — what it is and what it is for

Koog is JetBrains’ framework for building AI agents in Kotlin and Java. It focuses on practical architecture: strategies, tools, memory, tracing, long context and JVM production integration.

Guide

Prompt injection — hostile instructions in your context

Prompt injection is not a party-trick jailbreak. It is a boundary problem: the model reads untrusted text and may confuse it for instructions. With agents, it burns twice as hot.

Guide

RAG — Retrieval-Augmented Generation

When the model doesn't have your data in its head, it fetches it from a vector store or full-text search. RAG is a pattern, not a product.