Your Guide to Essential AI Concepts

Artificial Intelligence (AI) is no longer a futuristic concept. It is here. It is evolving at lightning speed. And it is reshaping nearly every aspect of our lives. From the recommendations you see online to the way businesses operate, AI is at the core. But with so much jargon floating around – LLMs, RAG, Embeddings, oh my! – it’s easy to feel lost.

This guide is designed to cut through the noise and explain the key AI concepts you need to know. Do you want to explain AI to your children? Then use our AI for kids guide!

AI Adoption

This refers to the process by which individuals, organisations, and industries integrate AI technologies and solutions into their existing workflows, products, and services. AI Adoption is about moving from pilot projects to widespread and impactful use of AI. Here are 10 principles to boost AI adoption and build a strong AI transformation strategy.

AI Agent

An autonomous entity that perceives its environment and takes actions to achieve specific goals. Agents can be software-based (like a chatbot booking flights) or physical (like a warehouse robot), using AI to make independent decisions. Read our guide on Microsoft AI Agents and AWS AI Agents.

AI Platforms

These are comprehensive ecosystems or software suites that provide the tools, infrastructure, and services necessary to develop, deploy, manage, and scale AI applications. They streamline the AI lifecycle, from data preparation to model deployment. Read more on AI Platforms

API (Application Programming Interface)

In an AI context, an API allows developers to easily integrate a pre-built AI model’s capabilities (like language generation or image recognition) into their own applications without needing to build the model from scratch. Read our guide on API-Driven AI: The Power of Connected Intelligence

Bias in AI

A systematic error in an AI system that produces prejudiced or unfair outcomes. Bias often stems from skewed training data that reflects existing human biases. It could lead to discriminatory results in areas like loan applications or hiring. Read our full guide on Bias in AI

(Agentic) Chunking

Chunking is the process of breaking down large documents into smaller, manageable pieces. This makes it easier for an AI to search through and find the specific information it needs.

Agentic Chunking is an advanced method where an AI itself decides how to break a document into logical, meaningful chunks. It uses its understanding of the content to group related ideas together, preserving context and coherence. Read our full guide on Chunking & Agentic Chunking

Clustering

An unsupervised learning technique used to group similar, unlabeled data points together into “clusters.” The goal is to discover natural structures and segments within the data, such as grouping customers by purchasing behaviour without prior labels.

Computer Vision

Computer vision is a field of AI that enables computers to interpret and understand information from digital images and videos. It allows machines to “see” and identify objects, people, and activities. Computer vision can power everything from facial recognition to self-driving cars.

Deep Learning

A subfield of machine learning based on artificial neural networks with many layers (hence “deep”). These complex, multi-layered networks allow models to learn intricate patterns from vast amounts of data, making them highly effective for tasks like speech and image recognition.

Classification

A supervised learning task where an AI model is trained to predict a categorical label. Its job is to sort inputs into predefined classes, such as identifying an email as “spam” or “not spam” or classifying an image as containing a “cat” or “dog.”

Embedding

In machine learning, an embedding is a low-dimensional vector representation of an item (like a word, image, or user) that captures its semantic meaning or relationships to other items. It allows AI models to process complex data efficiently by converting it into a numerical format.

Explainable AI (XAI)

Refers to methods and techniques that make the decisions and predictions of AI systems understandable and transparent to humans. It addresses the “black box” problem, helping users trust and effectively manage AI.

Fine-Tuning

The process of taking a pre-trained AI model (like a large language model) and further training it on a smaller, specific dataset to adapt it for a particular task or domain. This allows the model to become more specialized and perform better on specific challenges.

Generative AI

A subset of AI focused on creating new, original content, rather than just analyzing or classifying existing data. This includes generating text (like LLMs), images (e.g., Midjourney, DALL-E), audio, video, and even code.

(AI) Guardrails

In the context of AI, guardrails are protective mechanisms, rules, or policies implemented to ensure AI systems operate within defined boundaries, adhering to ethical guidelines, safety standards, and legal compliance. They prevent unintended or harmful behaviours. Read our full guide on AI Guardrails.

(AI & Model) Hallucination

A phenomenon where a generative AI model confidently produces outputs that are factually incorrect, nonsensical, or not grounded in its training data. Mitigating hallucinations is a key challenge for ensuring AI reliability. Read our guide on AI Hallucinations, understand its causes, and discover the strategies you can implement to tame these illusions and make AI a reliable partner.

Human in the Loop (HITL)

This is an approach to AI where human intervention is deliberately kept at certain stages of an AI system’s operation. This is crucial for tasks requiring nuanced judgment, ethical considerations, or when AI confidence levels are low, ensuring accuracy, safety, and accountability. Read our complete guide on Human In the Loop

LLM (Large Language Model)

These are powerful AI models trained on vast amounts of text data, enabling them to understand, generate, and process human language. Think of models like GPT-5 or Gemini – they can write essays, answer questions, summarise text, and even generate code.

Multimodal AI

AI systems capable of processing and understanding information from multiple data types—or “modalities”—simultaneously, such as text, images, and audio. This enables a more holistic understanding, allowing an AI to, for example, answer spoken questions about a video it is watching. Check our guide on Google Cloud’s multimodal AI

Neural Network

An AI model inspired by the structure of the human brain, consisting of interconnected layers of nodes or “neurons” that process information. Neural networks are the foundational architecture for most deep learning models and excel at recognizing patterns.

NLP (Natural Language Processing)

This is a branch of AI that enables computers to understand, interpret, and generate human language. NLP is what allows chatbots to comprehend your questions, translation tools to work, and sentiment analysis to gauge opinions.

Overfitting

A common pitfall in machine learning where a model learns its training data too well, including the noise and random fluctuations. This results in poor performance on new, unseen data because the model has failed to generalize and has instead memorized the training set.

Prompt Engineering

The art and science of crafting effective inputs (prompts) for AI models, especially large language models (LLMs), to elicit desired outputs. It’s about figuring out how to “talk” to an AI to get the best results. Discover our tips for prompt engineering.

Reinforcement Learning (RL)

A machine learning method where an AI “agent” learns by trial and error in an environment. The agent receives rewards for desired actions and penalties for undesired ones, allowing it to develop a strategy to maximize its reward over time, much like training a pet.

Regression

A supervised learning technique used to predict a continuous numerical value (e.g., predicting house prices, stock values, or temperature) based on input features. It identifies relationships between variables to make quantitative predictions.

Retrieval Augmented Generation (RAG)

A technique that enhances the capabilities of generative AI models (like LLMs) by allowing them to retrieve relevant information from an external knowledge base before generating a response. This helps reduce hallucinations and provides more accurate, up-to-date, and grounded answers. Read our complete guide on RAG.

Robotics

The field of engineering and computer science that deals with the design, construction, operation, and application of robots. While not solely AI, modern robotics heavily relies on AI for perception, navigation, decision-making, and interaction.

Supervised Learning

A type of machine learning where an algorithm learns from a dataset that includes “labelled” examples (input-output pairs). The model learns to map inputs to outputs by finding patterns in the labelled data, and then applies this learning to new, unlabeled data.

Tokenisation in AI

The foundational process of breaking down a stream of text into smaller units called “tokens” (which can be words, sub-words, or characters). This step is essential for language models to convert human language into a numerical format that they can analyse and process.

Transformer Architecture

A groundbreaking neural network design that is the foundation for most modern large language models (LLMs). Its key innovation is the “attention mechanism,” which allows the model to weigh the importance of different words in the input text to better understand context and relationships.

Unsupervised Learning

A type of machine learning where the algorithm learns from data that has not been labelled or categorised. The model’s goal is to find hidden patterns, structures, or anomalies within the data on its own, without being told what the “correct” answers are.

AI’s journey is just beginning, and with a solid understanding of AI’s core concepts, you’re well-equipped to participate in its exciting future. Keep learning, keep questioning, and keep exploring!