We’re entering a world in which AI is no longer a distant concept: it’s embedded in the way we work, learn, create, and interact. However, while tools are evolving fast, language often lags behind.
To navigate this transformation, we’ve put together a clear, business-focused glossary to help you understand the most important AI terms in 2025. Whether you’re building internal workflows, launching immersive training programs, or exploring automation through AI agents, this guide will be your gateway to a smarter, more agentic future.
LLM: Large Language Model
An LLM is a type of AI trained on massive volumes of text data to understand and generate language that sounds natural and human-like. These models—like OpenAI’s GPT-4o, Anthropic’s Claude, or Meta’s Llama—can answer questions, write articles, translate text, summarize documents, and more.
In business, LLMs power:
- Virtual assistants and chatbots
- Auto-generated reports
- Email drafting
- Knowledge base summarization
💡 Use case: a legal firm uses an LLM to summarize 50-page contracts in seconds, highlighting key clauses and risks.
MCP: Multimodal Conversational Platform
An MCP integrates different types of input—text, voice, image, and video—into a single AI interface. Instead of just chatting with words, users can upload a photo, ask a question about a chart, or receive a video response from the system.
These platforms are ideal for:
- Cross-modal customer support
- Interactive learning
- Visual diagnostics (e.g. in healthcare or manufacturing)
💡 Use case: a retail platform lets customers upload a photo of their living room, then suggests matching furniture in AR using an MCP.
AI Agent
AI agents are systems that autonomously complete tasks based on a user’s high-level goal. You tell it what to do, not how, and it figures out the steps, executes them, and reports back. Unlike traditional automation, agents can adapt their actions based on context and interact with APIs, databases, and even other agents.
They’re used in:
- Project management
- Procurement and invoicing
- Market research automation
We wrote a dedicated post about it: check it out here.
💡 Use case: a sales agent autonomously scans market trends, compiles a competitor report, and schedules an internal strategy meeting—no human input needed after the prompt.
Autonomous agents
An autonomous agent is an evolved form of AI agent. It doesn’t just wait for commands: it can self-initiate, monitor its environment, and take proactive decisions. It can correct itself, shift direction, or collaborate with other agents when encountering new information.
These agents are key in:
- Predictive maintenance in industry
- Fraud detection in finance
- Workflow orchestration in enterprise platforms
💡 Use case: a supply chain agent detects a delivery delay and reroutes orders automatically, alerting key stakeholders.
Prompt engineering
Prompt engineering is the process of crafting inputs that guide an AI model toward desired results. Since most models respond based on patterns, well-designed prompts can drastically improve accuracy, tone, or creativity.
Prompt engineering is used in:
- Copywriting and marketing automation
- Training data generation
- Conversational bot design
💡 Use case: an e-learning platform uses prompt templates to generate training quizzes tailored to each learner’s level and role.
Multi-Agent systems
A multi-agent system is a group of AI agents working together toward a common goal. Each agent may specialize in a specific task (e.g., one researches, one writes, one checks facts). This creates more complex and scalable automation, where tasks are delegated much like in human teams.
💡 Use case: a product launch campaign is coordinated by a group of AI agents—one handles press release drafts, another monitors social media trends, and another schedules outreach.
Vector database
This type of database stores information as vectors, numerical representations of meaning. Instead of relying on keyword matches, vector databases allow semantic search, which helps AI systems “understand” what you mean rather than what you type.
They are crucial for:
- Context-aware AI applications
- Personalization engines
- Memory for long-term interactions
💡 Use case: an immersive training platform uses a vector database to retrieve relevant learning modules based on user behavior and role.
Fine-tuning vs. prompting
- Fine-tuning involves training an LLM on your own data, so it “learns” your tone, context, or industry specifics.
- Prompting uses smart instructions to coax better results from general-purpose models.
💡 Use case: a customer service bot is fine-tuned on internal support tickets, but also uses prompting to personalize answers in real time.
Context window
This is the amount of information an LLM can hold “in mind” during a single conversation. A larger window (e.g., 128,000 tokens in GPT-4o) allows the model to reference longer documents or conversations without losing track.
💡 Use case: an AI assistant recalls a 50-page policy manual while answering questions in a live HR onboarding session.
RAG: Retrieval-Augmented Generation
RAG combines two powers: a search engine that finds external info, and an AI model that uses that info to generate accurate responses. It’s a way to extend AI’s knowledge in real time, making it useful in domains where factual accuracy is essential.
💡 Use case: a financial advisor chatbot uses RAG to pull real-time stock performance before giving investment insights.
Multimodal AI
Multimodal AI can interpret and produce content across formats: text, audio, video, and images. These models “understand” more like humans, allowing for more natural and efficient interactions.
💡 Use case: a virtual assistant can answer your voice question, analyze an uploaded chart, and reply with a narrated video summary.
As AI continues to evolve, so must our understanding of the terms shaping its future. Whether you’re designing immersive simulations, building smarter workflows, or evaluating how agentic systems can support your business, having a shared vocabulary is the first step toward meaningful innovation.
Curious to see how these technologies can fit into your strategy? Get in touch to find out how immersive, AI-powered solutions could open up new opportunities for your business.
