10 AI terms that will help you look like you're not completely lost

Source: Dev.to

Dog years

One year in the human world is roughly equivalent to five years when it comes to AI‑related technologies. If you started using an AI‑based tool in a project a couple of years ago, chances are that the technology you relied on is now completely obsolete. New tools and paradigms emerge every day, leaving behind concepts that were considered revolutionary just a few months ago.

Staying on top of things is a must in this industry. It’s no secret that you need one eye on the latest updates of the stack you’re working with, and another on the new technologies gaining traction, if you don’t want to fall out of the game. But this pace is dizzying even for those of us who are just trying to make a living—and pay our streaming subscriptions—in the software industry.

That’s why I’ve tried to put together a short list of terms that might be helpful if, like me, you have the feeling that this train is leaving without you (the paradox being that this list will very likely be outdated by the time you finish reading it).

1. Agentic AI

What it is not: an AI agent is not a chatbot. A chatbot responds to a prompt and stops there.

What it is: an agent operates more autonomously. It can:

1. Receive information from its environment (inputs, state, APIs)
2. Reason about that information
3. Decide which action to take
4. Execute the action
5. Observe the outcome and, if necessary, repeat the cycle

This perceive → reason → act → observe loop, together with some degree of memory and tool usage, is what differentiates an agent from a simple conversation with a language model. The approach can be applied to almost any programmatically definable context—from travel‑agency workflows to data analysis or DevOps engineering tasks.

2. Large Reasoning Model (LRM)

In most commercial chatbots the main priority is to deliver fast responses. That works well for simple tasks, but as problems become more complex multiple iterations are often required to reach a satisfactory result.

When the system takes longer to generate a response, users usually see the familiar “thinking…” message. This doesn’t mean a different model is being used; rather, the model is producing a longer output or following a more elaborate internal reasoning process.

Large Reasoning Models are models that are better tuned to:

• Break problems down into intermediate steps
• Maintain coherence throughout the reasoning process
• Handle tasks that require planning

Whether this behavior emerges from the model itself or is supported by external logic, it is exactly what AI agents need to operate effectively.

3. Vector Database

Even without a deep understanding of how LLMs work internally, it’s easy to see that the real challenge isn’t storing data—traditional databases are still perfectly fine for that—but rather being able to search and relate information by meaning, not just by exact matches.

Embedding Model

An embedding model transforms data such as text, images, or audio into numerical vectors. These vectors are mathematical representations that capture similarity relationships between different pieces of data.

What Is a Vector?

A vector is the numerical representation produced by an embedding model. It allows us to perform mathematical operations that find “nearby” vectors, which translates into searching and working with semantically similar content.

How vector databases work:

• In a traditional database, an image is stored as a BLOB (Binary Large Object). The database can retrieve the BLOB but knows nothing about its content.
• In a vector database, the same image is processed by an embedding model to generate a numerical vector.
• Those vectors occupy a high‑dimensional space where dimensions implicitly encode concepts (e.g., “landscape,” “mountains,” “people”). Images with similar content end up close to each other, making similarity search and comparison much easier.

4. RAG – Retrieval‑Augmented Generation

The typical workflow is:

1. A user submits a prompt.
2. The retriever converts the query into a vector using an embedding model.
3. The retriever performs a similarity search against a vector database and returns multiple relevant chunks of information.
4. Those chunks (now plain text, not vectors) are injected into the prompt sent to the LLM.

This allows the model to generate responses grounded in specific, up‑to‑date, or private context—without having been trained on that information beforehand.

5. MCP – Model Context Protocol

For an LLM to be truly useful, it needs to interact with external resources. It’s not enough for it to generate text in isolation; it must connect to databases, services, and other tools.

MCP is a pattern that defines how information from these sources is managed and channeled to the LLM. The MCP server acts as an intermediary between the model and external services (e.g., databases, email systems), so developers don’t have to reinvent the connection every time they want the LLM to access a new resource.

6. MOE – Mixture of Experts

MOE splits an LLM into multiple specialized subnetworks, called experts. A routing mechanism decides which expert(s) to activate for a given input, allowing the overall model to:

• Scale more efficiently
• Specialize in different domains or tasks
• Reduce inference cost by only using a subset of the total parameters

Bottom line

AI is moving at “dog‑year” speed. Keeping an eye on emerging concepts—agents, reasoning‑focused models, vector‑based retrieval, and architectural tricks like MOE—will help you stay relevant in a landscape that changes faster than the hardware it runs on.

7. Agentic RAG

If the concept of RAG is already somewhat abstract, Agentic RAG takes it a step further. While traditional RAG has simple retrieval, limited adaptability, and relies on static knowledge, Agentic RAG incorporates AI agents that can decide which tools to use, formulate retrieval strategies, and refine queries for more accurate and flexible responses.

High‑level workflow

1. Activate the appropriate expert(s) for a given task, so that only the necessary ones are used.
2. Each expert produces an output that is then combined, usually through a weighted sum determined by the routing mechanism.
3. The combined result is returned to the user.

This architecture allows scaling to models with billions of parameters without consuming all the model’s resources at each step, significantly optimizing efficiency and performance.

8. ASI (Artificial Super Intelligence)

And this is where one might be tempted to throw the computer out the window and consider a career as a farmer. The concept of ASI, as far as I know, is more theoretical than practical, but it exists and is important to keep in mind, especially given the speed at which technology evolves.

All the tools and concepts described so far—autonomous agents, databases that “understand” your photos, architectures that decide which expert to activate—slowly approach the paradigm of AGI (Artificial General Intelligence). Simplifying, AGI means systems capable of performing any task at the level of a human expert.

ASI goes a step further, implying a system with a much broader scope, capable of:

• Self‑improvement
• Solving problems “better” than a human expert
• Posing problems we cannot yet imagine

So, between autonomous agents, databases that “understand” your photos, architectures that decide which expert to activate, and the ever‑elusive promise of ASI, it’s hard not to feel a bit overwhelmed… and utterly fascinated at the same time.

The good news is, you don’t need to be a guru to hop on this train: all it takes is curiosity, patience, and a strong cup of coffee. Who knows, maybe in a few years my toaster will have its own RAG agent and recommend the perfect breakfast recipe while I’m still trying to figure out what my LLM is doing.