AI Technical Terms & Terminology For Beginners

AI is no longer just for engineers and researchers. Artificial intelligence is becoming familiar to people across every profession, from teachers and marketers to healthcare workers and designers. But familiarity is not the same as understanding. The moment you encounter AI in conversation or in the workplace, you quickly realise that technical terms can feel confusing.

This blog is designed to change that. By the end, you will be comfortable with the most important AI technical terms, and you will know how they connect to the tools you use every day.

What Is Artificial Intelligence?

Artificial intelligence, often shortened to AI, is the science of creating systems that perform tasks normally requiring human intelligence. This can include recognising speech, translating languages, spotting patterns in data, or even making decisions.

Unlike traditional software, which follows fixed instructions, AI systems adapt. They can learn from experience, improve over time, and respond flexibly to new data. That adaptability is what makes AI powerful and what makes its technical terms worth learning.

The Difference Between AI, Machine Learning and Deep Learning

Before we dive into the most common technical terms, it is useful to clarify the three layers of this field.

Artificial Intelligence (AI)
The broad concept of machines performing tasks that usually require human intelligence.

Machine Learning (ML)
A subset of AI where systems learn from data rather than following handwritten instructions. They improve performance by studying examples.

Deep Learning (DL)
A specialised form of machine learning that uses multi-layered neural networks to recognise very complex patterns, such as identifying faces in photos or translating entire passages of text.

Understanding these three terms is essential. Much of the technical language in AI builds on them.

10 Key AI Technical Terms to Know

1. Prompt

A prompt is the instruction or question you give an AI tool. For example, typing “Write me a summary of today’s news” is a prompt. The quality of the response depends on how well you phrase it. In practice, professionals are now developing prompt engineering skills to get more accurate and useful outputs from AI.

2. Chatbot

A chatbot is a program that can have a conversation with you through text or voice. Early chatbots followed scripts. Modern chatbots use natural language processing, allowing them to respond in ways that feel conversational and human-like.

3. Large Language Model (LLM)

A large language model is trained on vast amounts of text to understand and generate human-like language. Tools such as ChatGPT are examples. The term “LLM” has become a standard piece of technical vocabulary in AI research and business.

4. Generative AI

Generative AI creates new content; text, images, audio, or video, based on what it has learned. For businesses, this means writing content, generating graphics, or personalising marketing campaigns at speed. The emphasis is on “new”: generative AI is not copying, but producing original outputs from patterns in data.

5. Fine-Tuning

Fine-tuning means adapting a general AI model to a specific use case by training it with specialised data. For example, a legal firm might fine-tune a model with legal documents so it can answer case-related questions more accurately.

6. API (Application Programming Interface)

An API is a bridge that allows different software systems to communicate. In AI, APIs let businesses connect powerful models to their websites, apps, or services. This makes AI practical and scalable across industries.

7. Token

Tokens are the building blocks of text for AI systems. A token may be a full word, part of a word, or punctuation. When you interact with an AI, the system breaks your input into tokens and generates new tokens as output. Understanding tokens matters because most AI models set limits based on token count, not word count.

8. Training Data

Training data is the information used to teach AI models. For text models, this means books, articles, and websites. For image models, it means pictures paired with descriptions. The quality and diversity of training data determine how accurate and unbiased a model can be.

9. Hallucination

A hallucination is when AI produces information that sounds correct but is factually wrong. For example, it might invent a quote or create a false statistic. Recognising this limitation is important for anyone relying on AI in business or education.

10. Bias

Bias occurs when an AI produces skewed results because its training data was unbalanced. For example, if a hiring tool was trained only on data from one demographic, it might unfairly favour that group. Awareness of bias is a crucial step in applying AI responsibly.

Why Learning AI Technical Terms Matters

AI is not a passing trend. It is becoming part of daily life in search engines, digital assistants, content creation tools, and business analytics. Knowing the technical terms gives you confidence when:

• Discussing AI with colleagues, clients, or teachers

• Choosing the right tools for your work

• Understanding limitations such as bias or hallucination

• Exploring opportunities for automation and creativity

  
  

The more familiar you are with the language of AI, the more effectively you can apply it. A little knowledge in this area opens the door to much bigger opportunities.

Talk to the Professionals at Projekt Rising

Learning the technical terms is only the beginning. The real transformation happens when you put that knowledge into practice. At Projekt Rising, we help businesses move from understanding AI concepts to applying them in ways that improve efficiency, support creativity, and deliver measurable results. Alternatively, please see our case studies to learn how we have helped many brands improve their time management and efficiency using our AI toolkit.