Artificial Intelligence isn’t just the future; it’s the present. It’s updating our toothbrushes, transforming our workflows, and evolving at a breakneck pace that’s hard to keep up with, even for tech insiders.

To help you navigate this rapidly changing landscape, we’ve broken down seven essential AI terms that are critical to understanding where the technology is headed. How many do you know?

1. Agentic AI

You’ve likely heard the term “AI agents” everywhere. But what are they? Unlike a standard chatbot that responds to one prompt at a time, Agentic AI can reason and act autonomously to achieve complex, multi-step goals.

An AI agent operates in a continuous loop:

1. Perceive its environment.
2. Reason to determine the next best steps.
3. Act on its developed plan.
4. Observe the results and repeat.

These agents can act as your personal travel planner, a data analyst spotting trends in reports, or even a DevOps engineer fixing technical issues—all without constant human intervention.

2. Large Reasoning Models (LRMs)

Powering these advanced agents are Large Reasoning Models (LRMs). These are a specialized type of Large Language Model (LLM) that has undergone reasoning-focused fine-tuning.

While standard LLMs generate responses immediately, LRMs are trained to work through problems step-by-step. They are trained on problems with verifiably correct answers (like math or code) and learn to generate an internal “chain of thought.” Ever see a chatbot say “Thinking…”? That’s an LRM breaking down the problem before giving you a final, reasoned answer.

3. Vector Databases

To make AI truly knowledgeable, it needs access to vast amounts of data. This is where Vector Databases come in. They don’t store raw data like text or images directly. Instead, they use an embedding model to convert that data into vectors—long lists of numbers that capture the semantic meaning of the content.

The magic is in the search: by performing mathematical operations to find vectors that are close to each other, the database can find semantically similar content. For example, you can search with a picture of a mountain and find other similar landscapes, related articles, or even matching music.

4. RAG (Retrieval Augmented Generation)

RAG, or Retrieval Augmented Generation, is the practical application of vector databases that supercharges LLMs. It enriches a user’s prompt with relevant, real-time data from external sources.

Here’s how it works: A user asks a question (e.g., “What is our company’s paternity leave policy?”). The RAG system converts this question into a vector, performs a similarity search in a vector database (like your company’s internal handbook), and retrieves the relevant information. This context is then fed to the LLM, allowing it to generate a accurate, specific answer based on factual data, reducing the chance of hallucinations.

5. Model Context Protocol (MCP)

For an LLM to be truly useful, it needs to connect securely and standardizedly to external tools and data sources—from databases and email servers to code repositories.

The Model Context Protocol (MCP) is a standardized framework that defines how applications provide context to LLMs. Instead of developers building a new, one-off connection for every single tool, MCP provides a universal “plug” system. An MCP server acts as the intermediary, allowing the AI to safely and efficiently interact with any approved external system.

6. Mixture of Experts (MoE)

The Mixture of Experts (MoE) architecture is a clever and efficient way to scale up massive AI models without proportionally increasing computing costs. An MoE model is divided into many smaller, specialized neural subnetworks called “experts.”

A smart routing mechanism activates only the specific experts needed for a given task. The outputs of these activated experts are then merged. This means a model might have billions of total parameters but only uses a fraction of them for any single query. This allows for massive, powerful models that are surprisingly efficient to run.

7. ASI (Artificial Superintelligence)

Finally, we look to the theoretical horizon with ASI (Artificial Superintelligence). This is the ultimate goal of many frontier AI labs, though it does not exist today.

Our current best models are slowly approaching AGI (Artificial General Intelligence)—a theoretical AI that can perform any cognitive task as well as a human expert. ASI is the hypothetical next step: an intellect that would surpass human intelligence in every domain, potentially capable of recursive self-improvement. An ASI could continuously redesign and upgrade itself, leading to an intelligence explosion. This represents a future that could either solve humanity’s greatest challenges or create new ones we can’t yet conceive.

The Future is Now

These seven terms—from the practical RAG and MCP to the futuristic ASI—paint a picture of a field moving toward more autonomous, efficient, and powerful systems. Understanding this vocabulary is key to participating in the conversation about our AI-driven future.

Image : Lone Thomasky & Bits&Bäume / https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/