AI Terms You Should Know – From LLMs to AGI

AI is moving fast. Every week brings a new model, a new framework, or a new buzzword, and it’s easy to feel like you’re constantly playing catch-up. The good news is that you don’t need to track every release to stay relevant. You need to understand the fundamentals — the core concepts that show up in almost every modern AI application, regardless of which model or framework sits underneath.

This article breaks down 13 terms every developer should know in 2026, grouped into four categories: the foundations, how you work with AI, agentic AI concepts, and the frontier terms everyone’s talking about but few fully understand.

Note: These concepts build on each other. If you’re new to AI development, read them in order — each section assumes you understand the one before it.

These are the building blocks. Almost everything else in this article depends on understanding these three concepts first.

An LLM is the technology behind tools like ChatGPT, Claude, and Gemini. It’s a neural network trained on massive amounts of text, and through that training it learns statistical patterns in language — grammar, facts, reasoning steps, coding conventions, and more.

When you send a prompt, the model doesn’t “look up” an answer. It predicts the most likely next piece of text, one token at a time, based on everything it learned during training. That’s why LLMs can write code, summarize articles, or hold a conversation, even though they’re fundamentally doing next-token prediction.

Embeddings turn text, images, or other data into numerical vectors that capture meaning rather than just words. Two pieces of text with similar meaning end up close together in this vector space, even if they don’t share a single word.

This is why a search system built on embeddings knows that “forgot my password” and “can’t log in” are related. The words are completely different, but the underlying meaning is nearly identical.

A vector database is built specifically to store and search embeddings. Instead of matching exact keywords like a traditional database, it searches by similarity — finding entries whose meaning is closest to your query.

This is the technology that makes semantic search possible, and it’s a core piece of infrastructure behind RAG systems, which we’ll get to shortly.

Note: Popular vector databases include Pinecone, Weaviate, Qdrant, and pgvector (a PostgreSQL extension). The right choice usually depends on your scale, hosting preferences, and whether you want a dedicated vector store or an extension to a database you already run.

Once you understand what LLMs are, the next question is: how do you actually get good results out of them? These four concepts answer that.

The quality of your output is directly tied to the quality of your prompt. Prompt engineering is the practice of writing clear instructions, providing relevant context, giving examples, and setting constraints so the model understands exactly what you want.

A vague prompt produces a vague answer. A specific prompt with examples and constraints produces something you can actually use.

Context engineering is one of the most important AI skills right now, and it goes beyond a single prompt. It’s the discipline of deciding exactly what information a model receives before it generates a response — retrieved documents, conversation history, memory, available tools, and system instructions.

In practice, well-curated context often produces better results than simply switching to a bigger, more expensive model. If the model doesn’t have the right information in front of it, no amount of scale will fix that.

RAG combines a language model with a retrieval step. Instead of relying purely on what the model memorized during training, the system first searches a knowledge base — often a vector database — for relevant information, then feeds that information to the model as context before it generates an answer.

This matters because it grounds the model’s response in real, current data rather than whatever it happened to learn during training, which reduces hallucinations and keeps answers accurate even as your underlying data changes.

Fine-tuning changes how a model behaves by training it further on a specific set of examples. It’s not about giving the model more general knowledge — that’s what RAG is for. It’s about shaping tone, format, or specialized behavior that’s hard to achieve through prompting alone.

Note: A common mistake is reaching for fine-tuning when RAG would solve the problem more cheaply. If your issue is “the model doesn’t know about X,” that’s usually a retrieval problem, not a training problem.

This is where things get more autonomous. Agentic AI describes systems that don’t just respond — they act.

An AI agent goes beyond answering questions. It can plan, make decisions, call external tools, and complete multi-step tasks toward a goal with minimal human input. Where a standard LLM call is a single request and response, an agent operates in a loop, adjusting its actions based on what happens along the way.

The agentic loop describes how an agent actually operates: Observe → Reason → Act → Reflect → Repeat. Instead of producing a single response, the agent continuously evaluates its progress, checks the results of its previous action, and decides what to do next until the task is complete or it hits a stopping condition.

This loop is what separates an agent from a simple chatbot — it’s the mechanism that allows multi-step, goal-directed behavior.

MCP is an open standard, originally introduced by Anthropic in November 2024, that gives AI models a consistent way to connect to external tools, databases, APIs, and applications. Before MCP, connecting a model to each new tool or data source required a custom, one-off integration. MCP replaces that with a shared protocol: build a server once, and any MCP-compatible AI application can use it.

As of late 2025, Anthropic transferred governance of MCP to the Agentic AI Foundation under the Linux Foundation, making it a vendor-neutral standard rather than something owned by a single company. It’s now supported across major AI platforms, not just Claude

Think of it as a universal connector between AI systems and the tools they need to actually get work done.

Unlike a standard LLM that generates a response in one pass, a reasoning model spends extra computation working through a problem step by step before producing a final answer. This makes reasoning models especially strong at coding, math, planning, and other tasks that benefit from breaking a problem down rather than answering immediately.

These two terms get thrown around constantly, often loosely, so it’s worth being precise about what they actually mean and where today’s AI systems stand relative to them.

AGI refers to a hypothetical AI system that matches or exceeds human capability across virtually any cognitive task, not just the narrow domains today’s models are trained for. An AGI system would generalize knowledge, transfer skills between completely different domains, and solve novel problems without needing task-specific retraining.

It’s important to be clear here: today’s LLMs, including the most advanced reasoning models, are still classified as narrow AI (ANI). They can be extraordinarily capable within specific domains — writing code, passing exams, generating images — but they don’t yet demonstrate the flexible, cross-domain general intelligence that defines AGI. Researchers and major labs disagree sharply on timelines, with estimates ranging from a few years to a few decades.

ASI sits beyond AGI on the same continuum. Where AGI would match human-level intelligence, ASI describes a system that would surpass the combined cognitive abilities of all humans, across every domain, by a wide margin — scientific discovery, strategic planning, creativity, and social intelligence included.

ASI remains entirely theoretical. The path most researchers discuss involves recursive self-improvement: a system smart enough to improve its own design, producing a successor that’s smarter still. Whether that path is achievable, and on what timeline, is one of the most actively debated questions in AI research today.

Note: AGI and ASI are not interchangeable. AGI is about reaching human-level generality. ASI is about exceeding it by an unbounded margin. Neither has been achieved as of 2026.

	Capability	Status in 2026
ANI (Narrow AI)	Excels at specific, well-defined tasks (language, image recognition, coding)	Where all current AI systems, including LLMs, sit today
AGI (General AI)	Matches human-level intelligence across virtually any cognitive task	Not yet achieved; a stated goal of major AI labs
ASI (Superintelligence)	Surpasses all of humanity’s combined intelligence across every domain	Entirely theoretical; no agreed-upon timeline

AI terminology can feel overwhelming, but the underlying ideas are more stable than the headlines suggest. LLMs generate language from learned patterns. Embeddings and vector databases make meaning searchable. Prompt and context engineering shape what a model sees and how it responds. RAG and fine-tuning solve different problems — one adds knowledge, the other changes behavior. Agents and MCP extend models into autonomous, tool-using systems. And AGI and ASI remain distant, theoretical milestones that today’s narrow AI systems haven’t reached.

You don’t need to chase every new release to stay current. Understanding these 13 terms gives you a solid foundation for evaluating whatever comes next.