7 AI Terms You Need to Know: Agents, RAG, MOE & More
Artificial intelligence moves fast. One week, you are hearing about chatbots, the next week, everyone is talking about agents, vector databases, and things that sound like sci-fi concepts. If you are a developer, product manager, founder, or just someone building with AI, understanding these terms will help you follow conversations and make better technical decisions.
In this article, we will break down seven important AI terms in plain language using clear explanations and practical examples you can relate to.
1. Agentic AI
Agentic AI refers to AI systems that can take actions on their own to achieve a goal, rather than just responding to a single prompt.
Think of a normal chatbot. You ask a question, it answers, and the interaction ends. An agentic AI, on the other hand, can plan steps, make decisions, use tools, and keep going until a task is completed.
For example, imagine an AI job assistant. Instead of only answering “how do I write a CV?”, an agentic system could:
- Ask you about your background
- Search for relevant job listings
- Tailor your CV for each role
- Send applications
- Follow up with emails
All of this happens with minimal human input after the initial goal is set. Tools like AutoGPT, CrewAI, and many AI workflow agents are built around this idea.
In simple terms, agentic AI is about giving AI autonomy and responsibility, not just intelligence.
2. LLM (Large Language Model)
LLM stands for Large Language Model. This is the brain behind tools like ChatGPT, Claude, Ollama, and Gemini.
An LLM is trained on massive amounts of text so it can understand and generate human language. It does not “know” things the way humans do. Instead, it predicts the next word based on patterns it has learned.
For example, when you type:
“Write an email apologizing for a delayed response…”
The LLM predicts what a good continuation of that sentence looks like, based on millions of similar examples it has seen during training.
LLMs are great at:
- Writing and summarizing text
- Answering questions
- Explaining concepts
- Generating code
However, on their own, they can hallucinate or give outdated information. That is why other concepts like RAG and vector databases exist.
3. Vector Database
A vector database is a special type of database designed to store and search embeddings.
An embedding is a numerical representation of text, images, or other data. It captures meaning, not just keywords.
For example:
- “I love dogs”
- “I enjoy spending time with puppies”
These two sentences look different, but their embeddings are very similar because they mean almost the same thing.
A vector database allows you to store these embeddings and quickly find the most similar ones. Popular examples include Pinecone, Weaviate, Qdrant, and Chroma.
Vector databases are commonly used for:
- Semantic search
- Recommendation systems
- Long-term memory for AI agents
If your AI needs to “remember” or retrieve relevant information based on meaning, not exact words, you will likely need a vector database.
4. RAG (Retrieval Augmented Generation)
RAG stands for Retrieval Augmented Generation. It is a technique that helps LLMs give more accurate and up-to-date answers.
Here is the problem RAG solves. LLMs are trained on static data. They do not automatically know about your internal documents, your database, or new information after their training cut-off.
RAG works by:
- Retrieving relevant information from a data source, often using a vector database
- Injecting that information into the prompt
- Asking the LLM to generate an answer based on that context
For example, imagine a customer support chatbot for a fintech app. Instead of guessing answers, it:
- Searches your product documentation
- Pulls the most relevant sections
- Uses them to answer the user’s question
This makes responses more accurate, grounded, and trustworthy.
In short, RAG helps AI say “here is the answer based on your data” instead of “here is what I think sounds right.”
5. MCP (Model Context Protocol)
MCP stands for Model Context Protocol. It is a standard that allows AI models to securely access tools, data, and services in a structured way.
Think of MCP as a bridge between an AI model and the outside world.
Instead of hard-coding integrations for every tool, MCP defines how an AI can:
- Discover available tools
- Understand what each tool does
- Call those tools safely
- Receive structured responses
For example, an AI agent using MCP could connect to:
- Your file system
- A database
- A calendar
- An internal API
All without custom glue code for each integration.
MCP is important because, as AI agents become more capable, they need safe and consistent ways to interact with real systems. MCP helps make that possible.
6. MoE (Mixture of Experts)
MoE stands for Mixture of Experts. It is an architecture used to make large AI models more efficient and scalable.
Instead of one massive model doing all the work, MoE models are made up of many smaller “expert” models. For each input, only a few experts are activated.
Think of it like a company.
You do not ask the finance team to design your website, and you do not ask the designers to prepare tax reports. Each team specializes in something.
In the same way, an MoE model routes each task to the most relevant experts. This reduces compute costs while maintaining high performance.
MoE is one of the techniques used to build very large, powerful models without making them impossibly expensive to run.
7. ASI (Artificial Superintelligence)
ASI stands for Artificial Superintelligence. This refers to a hypothetical level of AI that surpasses human intelligence across almost all domains.
Unlike narrow AI, which is good at specific tasks, or general AI, which can reason across many tasks, ASI would:
- Learn faster than humans
- Solve problems humans cannot
- Improve itself autonomously
Today, ASI does not exist. Most of what we hear about it comes from research discussions, philosophy, and long-term AI safety debates.
It is still important to understand the term because many ethical and policy conversations around AI are really about preventing or preparing for ASI.
Final Thoughts
You do not need to know everything about AI to build useful products. But understanding these core terms will help you navigate conversations, choose the right tools, and avoid being lost in buzzwords.
If you are building with AI today, you are likely already touching LLMs, RAG, vector databases, and agentic systems, whether you realize it or not. Learning how they fit together is one of the best investments you can make as a builder.
AI will keep evolving. The best way to keep up is to understand the fundamentals and build from there.
