What Are Embeddings, Agent Skills, MCP, and OKF?

Five new blogs this week, covering the foundation behind modern AI, three open standards that make agents genuinely useful, and how we turn a general model into a specialist.

Let’s get started.

An embedding is a list of numbers that represents the meaning of something, arranged so that things with similar meaning get similar numbers.

In simple words, an embedding is meaning translated into numbers.

A computer is brilliant at comparing numbers, but it has no idea what words mean. So we hand the computer meaning in a form it is actually good at, which means numbers. The computer never had to understand English. It only has to notice that two lists of numbers are similar, and that is something a computer does perfectly.

Read here: https://outcomeschool.com/blog/what-are-embeddings

An Agent Skill is a folder of instructions, and optionally scripts and reference files, that an AI agent loads by itself only when the task actually needs it.

In simple words, a Skill is a set of written instructions that teaches an agent how to do one particular job well. We write it once, and the agent picks it up on its own whenever that job comes up.

The heart of it is progressive disclosure. At the start, the agent loads only the name and the description of each Skill. Only when our request matches does it read the full instructions. So the agent behaves as if it knows fifty procedures in depth, while its desk stays almost empty.

Read here: https://outcomeschool.com/blog/what-are-agent-skills

MCP, which stands for Model Context Protocol, is an open standard that defines one common way for AI applications to connect to outside tools and data.

An AI model, on its own, only knows how to produce text. It is like a very smart person locked inside a room with no phone and no internet. So, to make an AI model truly useful, we must connect it to the outside world.

In simple words, instead of every AI application inventing its own way to talk to every system, everybody agrees on one shared language. MCP is like a USB-C port for AI applications. And the most important point: the model requests, the host controls.

Read here: https://outcomeschool.com/blog/what-is-mcp-model-context-protocol

Fine-tuning is the process of taking a model that is already trained and training it a little more on our own specific data so that it becomes good at our specific task.

Fine-tuning means making small and careful adjustments to a model that already exists. We take a model that already knows a lot, and we teach it our special skill on top of that.

There are two common ways to do it. Full Fine-tuning adjusts every single weight in the model, while LoRA keeps the original weights frozen and only trains a small set of new, extra weights on the side, like adding a few sticky notes to a book that is already printed.

Read here: https://outcomeschool.com/blog/how-does-fine-tuning-work

OKF, which stands for Open Knowledge Format, is an open standard for writing down what an organization knows about its data and systems, as a folder of plain markdown files, so that any AI agent or any tool can read that knowledge without custom work.

In simple words, OKF is an agreed way of writing our company’s knowledge into ordinary text files, so that both people and machines can read the same file.

The key point is this: OKF does not store our data. It stores the understanding that sits around our data. The data is the what. The knowledge is the meaning. An agent reads the bundle before doing real work, so it stops guessing what our data means.

Read here: https://outcomeschool.com/blog/what-is-okf-open-knowledge-format

That’s it for now.