Artificial intelligence terms have flooded business meetings, LinkedIn posts, and corporate presentations — but not everyone actually knows what they mean. If you hear words like “LLM,” “prompt,” or “tokenization” and nod along uncertainly, this article is written for you. We have compiled 15 core concepts that will help you navigate the world of AI technologies with confidence no programming, no unnecessary jargon, just plain human language.

Basic Concepts of Artificial Intelligence: The Foundation

1. Artificial Intelligence (AI)

A broad concept describing computer systems capable of performing tasks that previously required human intelligence: speech recognition, decision-making, translation, text or image generation.

2. Machine Learning (ML)

A subfield of AI where a system learns from examples rather than following hard-coded rules. The algorithm analyzes large amounts of data and finds patterns independently. A simple example: an email spam filter learns to distinguish unwanted messages from useful ones based on thousands of examples.

3. Neural Network

A mathematical model inspired by the structure of the human brain. It consists of layers of “neurons” — information processing nodes. The more layers, the “deeper” the network. Neural networks power modern voice assistants and chatbots.

4. LLM (Large Language Model)

What is an LLM — a large language model: a type of neural network trained on enormous volumes of text (books, articles, websites, code). An LLM can understand and generate human language, answer questions, and hold conversations. Examples: GPT-4, Claude, Gemini.

5. Prompt

A prompt is a text instruction or query you send to an AI system. The quality of the response directly depends on the quality of the prompt. “Write text about a cat” and “Write a friendly Instagram post about a fluffy orange cat named Dream for an audience aged 25–35” represent two very different levels of prompting.

6. Token

A unit of text that a language model processes. A token is not necessarily a word — it can be part of a word, a space, or a punctuation mark. The number of tokens determines how much text the model can “see” and process at once. This is important for understanding the limitations of AI systems.

AI Dictionary: Technical Terms Made Simple

7. Hallucinations

One of the most important AI terms for businesses. Hallucinations occur when a model generates confidently worded but false or fabricated information. For example, an AI agent might “invent” a nonexistent law or state an incorrect product price. This is why quality support systems train agents to respond only on the basis of a verified knowledge base.

8. Context Window

The volume of text a model can “hold in memory” during a single conversation. If a dialogue exceeds the context window, the model “forgets” the beginning of the conversation. Modern LLMs have windows ranging from 32,000 to over one million tokens.

9. Embeddings

A mathematical representation of words or sentences in numerical space. This technology allows AI to “understand” the similarity of meanings: words like “car,” “automobile,” and “vehicle” will be placed close together in numerical space. Embeddings are the foundation of semantic search in knowledge bases.

10. RAG (Retrieval-Augmented Generation)

A method where AI first retrieves relevant information from a designated knowledge base, then generates a response based on it. RAG is what allows support AI agents to give accurate answers using company documentation without the risk of making things up.

11. Fine-tuning

The process of additional training of an already-prepared model on specific data from a particular company. After fine-tuning, the agent better understands the terminology, products, and specifics of the customer’s business.

12. AI Agent

An AI system capable not just of answering questions, but of performing actions: checking order status, scheduling appointments, updating CRM data. The key difference from a regular chatbot is the ability to interact with external systems.

Operational Terms: What Every Manager Needs to Know

13. No-code / Low-code

Approaches to building AI systems without programming or with minimal involvement. No-code platforms allow non-technical specialists to independently create and configure AI agents through a visual interface.

14. API (Application Programming Interface)

An interface for communication between software systems. The AI agent connects to a CRM, database, or messenger via API. For a support manager, understanding this means: if the chosen platform has an API, integration with the company’s existing tools is possible.

15. CSAT / NPS

Customer satisfaction metrics. CSAT (Customer Satisfaction Score) measures satisfaction with a specific interaction; NPS (Net Promoter Score) measures willingness to recommend the company. Both metrics are key for evaluating AI support effectiveness.

Now that the basic concepts of artificial intelligence are clear, the logical next step is to see them in action. The Intelswift platform is built on all the technologies described: RAG for accurate responses, a no-code builder for a quick start, hallucination control through logging, and a knowledge base instead of general models. Try it free for 14 days and turn terminology into practical results.