Dictionary of Artificial Intelligence (AI): Concepts, Definitions, and Business Applications

Dictionary of Artificial Intelligence (AI): A Complete Guide for Managers, Trainers, and Business Leaders

Artificial intelligence is no longer a futuristic concept — it is part of everyday work in every modern organization. From automated reports to personalized learning and intelligent chatbots, AI is transforming the way companies operate and make decisions.

But to make full use of AI, we need to know its language. The terms we encounter every day — „generative model“, „neural networks“, „RAG chatbot“, „local AI“ — can sound confusing if they are not explained in a human, clear, and structured way.

That is exactly what this article does. It brings together the most important concepts in artificial intelligence in one place — presented in understandable Bulgarian, with their Latin names and abbreviations, plus examples of real business applications.

This article is suitable for:
HR and L&D specialists
managers and executives
IT teams
consultants
trainers
companies planning or implementing AI systems
everyone who wants to work confidently with modern workplace technologies.

What is artificial intelligence and why is it important for business?

Artificial intelligence (Artificial Intelligence, AI) refers to systems capable of behavior that requires human intelligence — understanding, interpretation, analysis, prediction, learning, communication.

The difference between „traditional“ software and AI is huge:

• traditional software follows predefined rules

• AI systems learn from examples and improve their behavior over time

For business, this means:

• faster decisions

• automated processes

• personalized services

• improved internal communication

• better learning experiences

• reduced errors

• competitive advantage

Today, AI is widespread in almost every industry — from banks and telecom companies to manufacturing, education, healthcare, logistics, and marketing.

Core concepts in AI 

Algorithm (Algorithm)

A sequence of rules for solving a problem.

Example: a product recommendation algorithm in an online store.

Big Data (Big Data)

Huge amounts of information, often in real time, that are too large for traditional tools.

Business application: detecting patterns in consumer behavior.

Dataset (Dataset)

A structured or unstructured collection of data for training or analysis.

Machine Learning (Machine Learning, ML)

A method in which the system learns from examples.
We do not need to tell it every step — it extracts patterns on its own.

Deep Learning (Deep Learning, DL)

The most advanced form of ML, using deep neural networks.

Neural Network (Neural Network)

A simplified model of the brain, with many „neurons“ working together.

Generative AI (Generative AI, GenAI)

AI that can create new content — text, images, video, audio, code.

Examples:

• creating presentations

• writing texts

• generating scripts

• graphic design

Language Model (Language Model)

A model that understands and generates human language.

Large Language Model (Large Language Model, LLM)

Trained on huge text corpora.

It can:

• write

• analyze

• summarize

• translate

• solve tasks

Small Language Model (Small Language Model, SLM)

More compact, more economical, and suitable for local execution.

Graphics Processing Unit (Graphics Processing Unit, GPU)

Used for heavy computations and training AI models.

Training Data (Training Data)

What the model „learns“ from.

Embeddings (Embeddings)

A way to „translate“ content into numbers so it can be searched by meaning, not by word.

Vector Database (Vector Database, Vector DB)

A database for storing embeddings.
Used in modern RAG systems.

Probabilistic Model (Probabilistic Model)

A model that „predicts“ the next most likely action.

Fine-Tuning (Fine-Tuning)

Additional training of a ready model on specific data.

Example: training on company documents.

Hallucination (Hallucination)

When AI speaks confidently, but incorrectly.
A common reason: lack of up-to-date information.

Modern approaches in AI: from prompting to RAG

1. Prompting — the language we use to speak with AI

Prompt (Prompt)

An instruction given to the model.

Types of prompting:

• Zero-shot prompting
  No examples — the model uses „general knowledge“.

• One-shot prompting
  A single example of the desired format.

• Few-shot prompting
  Several examples for more precise guidance.

• Chain-of-Thought prompting
  The model describes its reasoning steps.

2. RAG — the safest way to use AI in business

Retrieval-Augmented Generation (Retrieval-Augmented Generation, RAG)

This is an approach that combines:
language model
+
knowledge base (vector database)

Advantages:

• fewer errors

• more up-to-date answers

• safer data processing

• personalized answers based on specific documentation

RAG is ideal for:

• internal chatbots

• self-service systems

• employee training

• customer support

• technical documentation

In practice, RAG is „AI with memory“ — and the memory is your documentation.

Special terms important for GDPR, security, and infrastructure

Local-first AI (Local-first AI)

AI that runs partially or entirely locally — on a phone, laptop, or local server.

Benefits:

• data control

• higher security

• lower risk of information leakage

• speed

On-premises AI (On-premises AI)

AI installed on the organization’s infrastructure.
Does not require external cloud services.

Used by: banks, telecom companies, public-sector organizations, institutions with high GDPR requirements

GDPR-safe AI (GDPR-safe AI)

Means:

• no sending personal data to external models

• transparency

• control over data flow

• risk minimization

• local processing

Autonomous agents — the next phase in AI

Autonomous Agent (Autonomous Agent)

An AI system that can:

• plan

• execute

• optimize

• correct

• learn

Without the need for a person to guide every step.

Examples:

• automation of back-office tasks

• process management

• AI assistants in CRM systems

• intelligent tools for sales

AI Tutor — a new era in corporate learning

AI Tutor (AI Tutor)

Role:

• explains complex topics

• adapts difficulty

• gives examples

• asks questions

• simulates real situations

• offers personalized learning paths

AI tutors are completely changing corporate training:

• more personal

• faster

• more interactive

• more accessible

Especially effective for blended learning and self-paced courses.

LMS with AI functionality

An intelligent LMS system is more than just an added AI feature. It is part of a broader architecture that enables:

• data collection and analysis

• content adaptation

• connection with business results

• personalized development

How to use this dictionary in your organization

For employee training

Use the terms as part of an internal academy or onboarding.

For team communication

Same language → better work.

For implementing AI solutions

Understanding the terminology shortens decision-making time and project cycles.

For content optimization

It can be included in presentations, workshops, and policy documents.

Artificial intelligence is developing faster than any previous technological wave. Companies that invest in knowledge and build a culture of digital understanding will have a huge advantage.

This dictionary serves as a solid foundation for working with AI — whether you are a trainer, manager, HR specialist, or expert in digital transformation.

If you are looking for training for your team or AI solution implementation, RAG chatbots, or personalized learning platforms, NIT – New Internet Technologies offers end-to-end solutions for business.
Contact us for consultation and a personalized approach.

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