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AI Glossary: All the Terms You Need to Know
A comprehensive AI Glossary, packed with must-know terms and definitions to keep you ahead in the world of artificial intelligence.
Last Updated: 2025-02-11
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Artificial Intelligence is shaking things up all over the place, and Asia’s bang in the middle of it with top-notch AI research, development, and innovation. In this friendly AI Glossary, you’ll find everything from the real basics to the seriously advanced—brilliant for getting a handle on AI and finding your way through this constantly expanding world.
How to Use: Feel free to hop straight to a letter in the table below or simply scroll away at your leisure. Each entry includes a handy difficulty level, a quick definition, everyday context, and links to related terms.
Table of Contents (tap a letter to jump right there)
A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z
~ A ~
AI (Artificial Intelligence)
Level: 🟢 Beginner
Definition: The field of computer science dedicated to creating machines or software that can perform tasks requiring human-like intelligence (e.g., problem-solving, learning).
Context: Encompasses various subfields like Machine Learning, NLP, and Robotics.
See Also: AGI, Machine Learning, Deep Learning
AGI (Artificial General Intelligence)
Level: 🔴 Advanced
Definition: A still-hypothetical form of AI with the ability to understand, learn, and apply knowledge across a broad range of tasks at a level comparable to humans.
Context: Considered the “holy grail” of AI research; raises significant ethical, societal, and philosophical questions.
See Also: AI, Ethics
Accuracy
Level: 🟢 Beginner
Definition: The proportion of correct predictions made by a model out of all predictions.
Context: Commonly used in classification; may be misleading on imbalanced datasets.
See Also: Precision, Recall
Accuracy
Activation Function
Level: 🟡 Intermediate
Definition: A mathematical function that determines the output of a neuron in a neural network, introducing non-linearity.
Context: Common types include ReLU, Sigmoid, and Tanh; essential for Deep Learning.
See Also: Neural Network, Deep Learning
Active Learning
Level: 🟡 Intermediate
Definition: An AI strategy that smartly mixes supervised and unsupervised learning, but only shouts for human help when it really needs it.
Context: Cuts down on repetitive labelling tasks by focusing human effort where it matters most, making the whole learning process a bit less of a slog.
See Also: Supervised Learning, Unsupervised Learning
Adversarial Attack
Level: 🔴 Advanced
Definition: Methods to deceive AI models (especially neural networks) by subtly altering inputs to cause incorrect outputs or classifications.
Context: Important in security-sensitive AI applications like biometric authentication.
See Also: Robustness, GAN
Algorithm
Level: 🟢 Beginner
Definition: A set of rules or steps for solving a problem or performing a computation.
Context: In AI, algorithms govern how models learn from data.
See Also: Model, Machine Learning
AutoML (Automated Machine Learning)
Level: 🟡 Intermediate
Definition: Tools and frameworks that automate the ML pipeline, including feature engineering, model selection, and hyperparameter tuning.
Context: Democratizes AI by reducing the expertise required for building effective models.
See Also: Hyperparameter Optimization, NAS
~ B ~
Backpropagation
Level: 🟡 Intermediate
Definition: An algorithm for training neural networks by updating weights in reverse from the output layer to the input layer.
Context: Essential to Deep Learning; works in tandem with Gradient Descent.
See Also: Gradient Descent, Neural Network
Bayesian Network
Level: 🔴 Advanced
Definition: A probabilistic model using a directed acyclic graph to represent variables and their conditional dependencies.
Context: Useful for uncertainty modeling and inference in complex domains.
See Also: Probabilistic Models
Bias
Level: 🟢 Beginner
Definition: Systematic errors in model predictions due to skewed data, flawed assumptions, or societal inequities mirrored in the training set.
Context: Can lead to discriminatory outcomes in areas like hiring, lending, or healthcare.
See Also: Fairness, Ethics
Big Data
Level: 🟢 Beginner
Definition: Extremely large and complex datasets that require specialized methods to store, process, and analyze.
Context: Underpins many AI and ML advancements by providing ample training data.
See Also: Data Mining, Data Science
~ C ~
Character.AI
Level: 🤖 Chatbot
Definition: A platform where you can chat with AI “characters” that have been trained to role-play as famous figures, fictional personas, or even brand-new personalities you dream up.
Context: Great for imaginative fun or storytelling—just watch out for those moments when the AI might spin you a tall tale!
Try It: Character.ai/
See Also: Prompt, Chatbot
Chatbot
Level: 🟢 Beginner
Definition: An AI-driven software application that simulates human conversation, often via text or voice interfaces.
Context: Commonly used in customer service, marketing, and information retrieval.
See Also: Conversational AI, Transformer
ChatSonic (by Writesonic)
Level: 🤖 Chatbot
Definition: A GPT-powered chatbot that helps you write blogs, emails, or social posts in a jiffy—think of it as your personal writing sidekick.
Context: Comes with real-time web browsing built in, so it can pull in updated facts and figures instead of relying on pre-trained data alone.
Try It: Writesonic Chat
See Also: Prompt, Chatbot
Claude (by Anthropic)
Level: 🤖 Chatbot
Definition: A conversational AI assistant from the folks at Anthropic—imagine ChatGPT’s clever sibling, but with a huge focus on dodging harmful or dodgy content.
Context: Delivers context-rich responses while aiming to curb “hallucinations.”
Try It: Claude AI
See Also: Prompt, Chatbot
Classification
Level: 🟢 Beginner
Definition: A supervised learning task where the model predicts a discrete category (e.g., spam vs. not spam).
Context: Measured by Accuracy, Precision, Recall, or F1-score.
See Also: Logistic Regression, Model
Clustering
Level: 🟡 Intermediate
Definition: An unsupervised learning method grouping similar data points together without pre-labeled categories.
Context: Useful in customer segmentation, anomaly detection, and data exploration.
See Also: K-Means, Unsupervised Learning
Computer Vision
Level: 🟡 Intermediate
Definition: A field of AI that enables machines to interpret and understand visual data, like images and videos.
Context: Powers facial recognition, self-driving cars, and medical image analysis.
See Also: Neural Network, YOLO
Copilot (formerly Bing Chat)
Level: 🤖 Chatbot
Definition: Microsoft’s AI-powered assistant, formerly known as Bing Chat, now built right into various Microsoft apps to offer real-time info, creative suggestions, and Q&A.
Context: Think of it as your on-the-spot AI helper—answering questions, brainstorming ideas, and even debugging code inside your favourite Microsoft products.
Try It: Copilot
See Also: Prompt, Chatbot
Conversational AI
Level: 🟡 Intermediate
Definition: AI systems designed to interact with humans via natural language, often combining speech recognition, NLP, and dialogue management.
Context: Used in virtual assistants (e.g., Alexa, Siri) and advanced customer support bots.
See Also: Chatbot, NLP
~ D ~
Data Augmentation
Level: 🟡 Intermediate
Definition: Techniques to increase the diversity of the training dataset (e.g., flipping images, adding noise) without collecting new data.
Context: Improves model robustness, especially in Computer Vision or Speech Recognition.
See Also: Data Mining
Level: 🖼️ Image Generation
Definition: An AI tool that conjures up images from text prompts—anything you can describe, DALL-E tries to illustrate.
Context: Great for quick concept art, quirky designs, or just having a laugh imagining “otters in outer space.”
Try It: Dall-e-3
See Also: Generative AI, Prompt
Data Mining
Level: 🟢 Beginner
Definition: The process of discovering patterns and insights in large datasets using methods at the intersection of machine learning and statistics.
Context: Often a precursor to model building in AI projects.
See Also: Big Data, Data Science
Data Science
Level: 🟢 Beginner
Definition: An interdisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge from data.
Context: Data scientists often employ AI/ML techniques for predictive analytics.
See Also: Machine Learning, Big Data
Dataset
Level: 🟢 Beginner
Definition: A collection of data points—like text, images, or numbers—used to train and test AI models.
Context: Think of it as the “study material” for AI. High-quality, well-structured datasets help models learn more accurately.
See Also: Training, Data Augmentation
Deep Learning
Level: 🟡 Intermediate
Definition: A subset of ML that leverages neural networks with multiple layers to model complex patterns in data.
Context: Drives major AI breakthroughs in image recognition, NLP, and more.
See Also: Neural Network, Backpropagation
DeepSeek
Level: 🤖 Chatbot
Definition: An advanced AI search assistant that blends deep language smarts with search capabilities, aiming to dig up precise info faster than you can say “query.”
Context: Great for in-depth research tasks, though it can still slip up with made-up details now and then.
Try It: DeepSeek
See Also: Prompt, Chatbot
Differential Privacy
Level: 🔴 Advanced
Definition: A mathematical framework that adds noise to datasets or outputs to protect individual data points while allowing aggregate analysis.
Context: Critical for compliance with regulations (e.g., GDPR) and safeguarding sensitive data.
See Also: Privacy, Federated Learning
~ E ~
Edge AI
Level: 🟡 Intermediate
Definition: Running AI algorithms locally on edge devices (e.g., smartphones, IoT sensors) rather than centralized servers.
Context: Reduces latency, saves bandwidth, and often enhances privacy.
See Also: Edge Computing, TinyML
Edge Computing
Level: 🟢 Beginner
Definition: Processing data near the source (e.g., IoT devices) instead of sending it all to the cloud.
Context: Improves response times and data handling efficiency.
See Also: Edge AI, IoT
Ensemble Learning
Level: 🟡 Intermediate
Definition: A technique that combines multiple models to improve overall predictive performance.
Context: Methods include Bagging, Boosting, and Stacking.
See Also: Random Forest, Gradient Descent
Level: 🟢 Beginner
Definition: One complete pass through the entire training dataset by an AI model.
Context: If you go over your dataset multiple times (multiple epochs), your model might learn patterns more thoroughly—unless it overfits.
See Also: Overfitting, Training
Ethics
Level: 🟢 Beginner
Definition: Moral principles guiding AI’s design and deployment, emphasizing transparency, fairness, and accountability.
Context: Especially relevant in fields like healthcare, finance, or autonomous vehicles where AI decisions have critical impact.
See Also: Fairness, Bias
~ F ~
Fairness
Level: 🟢 Beginner
Definition: Ensuring AI outcomes do not discriminate or harm certain groups, especially vulnerable or historically disadvantaged populations.
Context: Closely tied to bias mitigation and ethical AI guidelines.
See Also: Bias, Ethics
Federated Learning
Level: 🔴 Advanced
Definition: A decentralized ML approach where devices train local models and share only aggregated updates, not raw data.
Context: Maintains data privacy and is suitable for large-scale, distributed systems (smartphones, IoT).
See Also: Edge AI, Differential Privacy
Feature Engineering
Level: 🟡 Intermediate
Definition: The process of selecting, creating, or transforming input variables (features) to improve model performance.
Context: Vital in traditional ML workflows; neural networks often learn features automatically.
See Also: Model
Few-Shot Learning
Level: 🔴 Advanced
Definition: Techniques enabling models to generalize from only a small number of labeled examples.
Context: Crucial in scenarios where data collection is expensive or limited.
See Also: Zero-Shot Learning, Meta-Learning
Fine-Tuning
Level: 🟡 Intermediate
Definition: Taking a model that’s already been trained on a broad dataset and giving it extra training on more specific data, so it gets really good at a narrower task.
Context: Common with large language models—why start from scratch when you can build on existing knowledge?
See Also: Transfer Learning, LLM
F1-Score
Level: 🟡 Intermediate
Definition: The harmonic mean of precision and recall, offering a single metric when dealing with class imbalance.
Context: Often used in classification to balance false positives and false negatives.
See Also: Precision, Recall
~ G ~
GAN (Generative Adversarial Network)
Level: 🔴 Advanced
Definition: A neural network framework with two models—Generator and Discriminator—competing to create realistic data.
Context: Generates synthetic images, text, or other data; used in art, game design, and data augmentation.
See Also: Adversarial Attack, Unsupervised Learning
Gemini (by Google)
Level: 🤖 Chatbot
Definition: Google’s next-generation AI model, taking over where Bard left off—like your genius mate who’s absorbed Google’s entire knowledge base and can chat on any topic.
Context: Touted as a major leap forward in conversational AI, backed by Google’s vast machine learning resources.
Try It: Google Gemini
See Also: Prompt, Chatbot
Generative AI
Generative AI
Level: 🟡 Intermediate
Definition: AI that doesn’t just analyse data, but actually creates brand-new content—like text, images, and even music.
Context: Powering everything from AI art apps to chatbots, it’s shaking up industries with fresh ways to produce, well, just about anything.
See Also: GAN, LLM, Transformer
GPT (Generative Pre-Trained Transformer)
Level: 🔴 Advanced
Definition: A type of large language model (LLM) that harnesses the Transformer architecture to generate remarkably coherent text—famously exemplified by OpenAI’s GPT series.
Context: Known for its massive scale and wide-ranging capabilities, from drafting emails to crafting code. GPT models underscore just how far AI can go in churning out human-like content.
See Also: Large Language Model (LLM), Transformer
GPU (Graphics Processing Unit)
Level: 🟢 Beginner
Definition: A specialised chip originally made for rendering graphics, but brilliant at handling the parallel computations needed for AI tasks.
Context: Speeds up training large models—basically, the horsepower behind modern AI breakthroughs.
See Also: Edge AI, Deep Learning
Gradient Descent
Level: 🟡 Intermediate
Definition: An optimization algorithm that adjusts model parameters to minimize a cost (loss) function by following the gradient.
Context: Integral to training most neural networks, with variants like Stochastic and Mini-Batch.
See Also: Backpropagation, Loss Function
Grok (by xAI)
Level: 🤖 Chatbot
Definition: A conversational assistant from Elon Musk’s xAI venture, aiming to bring a fresh twist to the AI chatbot scene—still in its early days but garnering heaps of attention.
Context: Positioned as a direct rival to heavyweights like ChatGPT, with an eye on open, unfiltered conversations.
Try It: X ai
See Also: Prompt, Chatbot
~ H ~
Hallucination
Level: 🟡 Intermediate
Definition: When an AI system churns out information that sounds perfectly credible but is actually made-up—essentially presenting fiction as fact.
Context: Often pops up in large language models, highlighting the need for verification and fact-checking, especially in high-stakes environments.
See Also: XAI, Bias
Heuristic
Level: 🟢 Beginner
Definition: A rule-of-thumb or approximate method for quickly finding a solution when exact algorithms are impractical.
Context: Commonly used in AI search problems (e.g., pathfinding, game AI).
See Also: Algorithm
HuggingChat (by Hugging Face)
Level: 🤖 Chatbot
Definition: An open-source chatbot platform run by Hugging Face, letting you converse with cutting-edge AI models without the usual paywalls or sign-ups.
Context: Ideal for users who want a free, community-driven environment to experiment with AI text generation.
Try It: Hugging Face Chat
See Also: Prompt, Chatbot
Hyperparameter
Level: 🟡 Intermediate
Definition: A parameter set before training (e.g., learning rate, number of layers) that controls the training process.
Context: Fine-tuning hyperparameters is crucial for model performance.
See Also: Hyperparameter Optimization
Hyperparameter Optimization
Level: 🔴 Advanced
Definition: Techniques (like grid search, random search, Bayesian optimization) for finding the best hyperparameter configurations.
Context: Can drastically improve model accuracy and speed.
See Also: AutoML, Gradient Descent
~ I ~
Ideogram
Level: 🖼️ Image Generation
Definition: A text-to-image generator that claims to handle even the trickiest text-based prompts, focusing on clearer typography in AI-generated designs.
Context: Useful if you need stylised words and graphics in your AI images, rather than just abstract scenes.
Try It: https://ideogram.ai/
See Also: Generative AI, Prompt
Inference
Level: 🟢 Beginner
Definition: The process of using a trained model to make predictions on new, unseen data.
Context: Often optimized separately from training for speed, especially in production.
See Also: Model
IoT (Internet of Things)
Level: 🟢 Beginner
Definition: A network of connected devices (e.g., sensors, appliances) that collect and exchange data.
Context: AI at the edge (Edge AI) can process IoT data in real time for applications like smart homes, industrial monitoring.
See Also: Edge Computing
~ J ~
Jupyter Notebook
Level: 🟢 Beginner
Definition: An open-source web tool that allows you to combine live code, visualizations, and narrative text in a single document.
Context: Widely used in data science and AI for exploratory analysis and prototyping.
See Also: Python, Data Science
~ K ~
K-Means
Level: 🟢 Beginner
Definition: A popular clustering algorithm partitioning data into K distinct clusters by minimizing within-cluster variance.
Context: Often a go-to algorithm for quick segmentation tasks.
See Also: Clustering, Unsupervised Learning
Knowledge Graph
Level: 🟡 Intermediate
Definition: A structure of entities (nodes) and their relationships (edges) representing real-world facts.
Context: Used by search engines (e.g., Google) for better data connectivity.
See Also: Ontology, Semantic Web
~ L ~
Large Language Model (LLM)
Level: 🟡 Intermediate
Definition: A neural network (often with billions of parameters) trained on massive text corpora to understand and generate language.
Context: Powers advanced NLP tasks, including chatbots, summarization, and translation.
See Also: Transformer
Learning Rate
Level: 🟡 Intermediate
Definition: A hyperparameter controlling how much model weights update during each training step.
Context: Too large can cause divergence; too small can slow learning.
See Also: Gradient Descent
Logistic Regression
Level: 🟢 Beginner
Definition: A statistical model often used for binary classification (e.g., predicting yes/no outcomes).
Context: Known for its simplicity and interpretability; often a baseline for more complex models.
See Also: Classification
Loss Function
Level: 🟡 Intermediate
Definition: A measure of how far a model’s predictions deviate from actual targets.
Context: Guides the learning process via Gradient Descent.
See Also: Training
~ M ~
Machine Learning (ML)
Level: 🟢 Beginner
Definition: A subset of AI that allows systems to learn from data and make predictions or decisions with minimal human intervention.
Context: Encompasses supervised, unsupervised, and reinforcement learning.
See Also: Deep Learning, Data Science
Midjourney
Level: 🖼️ Image Generation
Definition: A Discord-based AI art generator famous for producing jaw-dropping, highly detailed images—just type in your prompt, and watch the magic happen.
Context: Hugely popular among digital artists and casual doodlers alike; known for an imaginative, painterly aesthetic.
Try It: https://midjourney.com/
See Also: Generative AI, Prompt
Meta AI
Level: 🤖 Chatbot
Definition: Meta’s (formerly Facebook) AI chatbot, geared towards a broad user base across Messenger, Instagram, and more—like a digital mate that can help with queries or just have a natter.
Context: Part of Meta’s larger ambition to weave AI assistants into everyday social interactions, not just standalone apps.
Try It: Meta AI
Meta-Learning
Level: 🔴 Advanced
Definition: “Learning to learn,” where a model leverages knowledge from previous tasks to learn new tasks more efficiently.
Context: Useful in Few-Shot or Zero-Shot Learning scenarios.
See Also: Few-Shot Learning, Transfer Learning
Mistral
Level: 🤖 Chatbot
Definition: A newly developed AI model focussed on open-source collaboration, designed to deliver top-notch results without ballooning into a massive, unwieldy giant.
Context: Perfect for those who like to tinker—ideal if you want to tweak or fine-tune a chatbot under the hood.
Try It: Le Chat
See Also: Prompt, Chatbot
Model
Level: 🟢 Beginner
Definition: A mathematical representation of a real-world process, trained from data to perform specific tasks (e.g., classification).
Context: Types include neural networks, decision trees, and linear models.
See Also: Training, Inference
Multi-Task Learning
Level: 🔴 Advanced
Definition: A learning paradigm in which a single model is trained to handle multiple tasks simultaneously.
Context: Can improve performance by leveraging shared representations across tasks.
See Also: Transfer Learning
My AI (by Snapchat)
Level: 🤖 Chatbot
Definition: Snapchat’s built-in AI buddy, ready to chat about your day, suggest filters, or even join group convos for a bit of fun banter.
Context: It’s integrated right into Snapchat, so no separate app needed—handy for on-the-fly chats and quick tips.
Try It: Snapchat My AI
See Also: Prompt, Chatbot
~ N ~
NAS (Neural Architecture Search)
Level: 🔴 Advanced
Definition: Automated methods to discover optimal neural network architectures, often outperforming manually designed networks.
Context: Very computationally intensive but can yield state-of-the-art results.
See Also: AutoML, Hyperparameter Optimization
Natural Language Processing (NLP)
Level: 🟢 Beginner
Definition: A branch of AI focusing on the interaction between computers and human language.
Context: Tasks include sentiment analysis, machine translation, and text summarization.
See Also: Transformer, Tokenization
Neural Network
Level: 🟡 Intermediate
Definition: A computational model inspired by biological neurons, consisting of layers that learn hierarchical representations of data.
Context: The core of Deep Learning.
See Also: Deep Learning, Activation Function
NLP Pipeline
Level: 🟢 Beginner
Definition: The sequence of steps (e.g., tokenization, part-of-speech tagging) applied to text data in NLP.
Context: Ensures consistent processing of language data prior to modeling.
See Also: NLP
~ O ~
Ontology
Level: 🟡 Intermediate
Definition: A formal structure representing concepts within a domain and relationships between them.
Context: Used in semantic web, knowledge graphs, and advanced AI reasoning.
See Also: Knowledge Graph, Semantic Web
Overfitting
Level: 🟢 Beginner
Definition: When a model learns the training data’s noise or details too well, harming generalization to new data.
Context: Common solutions include regularization, cross-validation, or early stopping.
See Also: Underfitting, Regularization
~ P ~
Pi (by Inflection AI)
Level: 🤖 Chatbot
Definition: A personal AI companion from Inflection AI that aims to offer a friendly, chat-based approach to everything from scheduling reminders to life advice.
Context: Designed for one-on-one help, focusing on emotional understanding as well as factual answers—sort of a cross between a notepad and a confidant.
Try It: https://heypi.com/
See Also: Prompt, Chatbot
Poe (by Quora)
Level: 🤖 Chatbot
Definition: A chatbot aggregator where you can switch between multiple AI models (including GPT variants) in one place—like a sampler platter of AI engines.
Context: Perfect if you’re curious to test the quirks of different chatbots without hopping between loads of sites.
Try It: https://poe.com/
See Also: Prompt, Chatbot
Precision
Level: 🟡 Intermediate
Definition: The fraction of predicted positives that are truly positive.
Context: Important when false positives are costly (e.g., medical diagnoses).
See Also: Recall, F1-Score
Privacy
Level: 🟢 Beginner
Definition: Protecting personal or sensitive data from unauthorized access or disclosure, often through techniques like encryption or differential privacy.
Context: Critical in healthcare, finance, and any application handling sensitive user data.
See Also: Differential Privacy, Federated Learning
Probabilistic Models
Level: 🟡 Intermediate
Definition: Models that incorporate probability distributions to handle uncertainty (e.g., Bayesian networks).
Context: Useful in domains like finance, weather forecasting, and medical diagnostics where data is noisy or incomplete.
See Also: Bayesian Network
Prompt
Level: 🟢 Beginner
Definition: A piece of text (or sometimes an image or other input) given to an AI model to guide its response or output.
Context: In chatbots like ChatGPT, the prompt is essentially your question or statement—framing it clearly can help the AI produce a more accurate or creative answer.
See Also: Prompt Engineering, Generative AI
Prompt Engineering
Level: 🟡 Intermediate
Definition: Crafting clever inputs or questions so an AI model (especially a language model) gives you the most useful answers.
Context: Hugely important in conversational AI—getting the prompt right can be the difference between a spot-on reply and a confusing jumble.
See Also: GPT, Generative AI
Python
Level: 🟢 Beginner
Definition: A high-level programming language widely used in AI/ML due to its readability and extensive libraries (NumPy, Pandas, PyTorch, TensorFlow).
Context: De facto standard for AI prototyping and production.
See Also: Jupyter Notebook
~ Q ~
Q-Learning
Level: 🔴 Advanced
Definition: A reinforcement learning technique that learns a quality function (Q) for actions in given states.
Context: Popular in game AI and robotics for sequential decision-making tasks.
See Also: Reinforcement Learning
Quantum Computing
Level: 🔴 Advanced
Definition: Computing based on quantum-mechanical phenomena, enabling potentially vast speedups for certain algorithms.
Context: Could revolutionize AI for optimization, cryptography, and complex simulations.
See Also: Algorithm
~ R ~
Random Forest
Level: 🟡 Intermediate
Definition: An ensemble method combining many decision trees to improve prediction accuracy and reduce overfitting.
Context: A robust, commonly used baseline model.
See Also: Ensemble Learning
Recall
Level: 🟡 Intermediate
Definition: The fraction of actual positives that are correctly identified by the model.
Context: Crucial when missing positives is costly (e.g., disease detection).
See Also: Precision, F1-Score
Recommender System
Level: 🟡 Intermediate
Definition: Algorithms that predict user preferences to suggest relevant items (e.g., movies, products).
Context: Widely used in e-commerce, streaming services, and social media.
See Also: Machine Learning
Regularization
Level: 🟡 Intermediate
Definition: Techniques (L1, L2, Dropout) to prevent overfitting by penalizing complex models.
Context: Helps models generalize better to unseen data.
See Also: Overfitting, Loss Function
Reinforcement Learning
Level: 🔴 Advanced
Definition: A learning paradigm where an agent learns to perform actions in an environment to maximize a cumulative reward.
Context: Used in robotics, gaming (AlphaGo), and self-driving vehicles.
See Also: Q-Learning
Replika
Level: 🤖 Chatbot
Definition: A virtual companion app where you can chat and bond with an AI “friend” that learns your interests, personality, and conversation style over time.
Context: Emphasises emotional support and personal connection, more than just fetching facts or drafting content.
Try It: Replika
See Also: Prompt, Chatbot
Robustness
Level: 🟡 Intermediate
Definition: The ability of an AI model to maintain performance under adversarial inputs or variations in data.
Context: Critical for safety-critical applications like autonomous driving or healthcare.
See Also: Adversarial Attack
~ S ~
Semantic Web
Level: 🟡 Intermediate
Definition: A framework for sharing data across the web in a machine-readable format, enabling better data interoperability.
Context: Facilitates AI systems in understanding and linking information.
See Also: Ontology, Knowledge Graph
Stable Diffusion
Level: 🖼️ Image Generation
Definition: An open-source image model that translates text prompts into high-quality visuals, all without relying on proprietary code.
Context: Perfect for DIY enthusiasts who want to run AI art on their own hardware, or customise the model under the hood.
Try It: Stability AI
See Also: Generative AI, Prompt
Supervised Learning
Level: 🟢 Beginner
Definition: A type of ML where the model learns from labeled data (input-output pairs).
Context: Includes tasks like classification and regression.
See Also: Unsupervised Learning, Reinforcement Learning
Support Vector Machine (SVM)
Level: 🟡 Intermediate
Definition: A supervised learning algorithm finding the optimal hyperplane to separate different classes in high-dimensional space.
Context: Effective for medium-sized, well-structured datasets.
See Also: Classification
~ T ~
Test Set
Level: 🟢 Beginner
Definition: A final set of data used to evaluate the performance of a fully trained model.
Context: Essential for unbiased performance assessment.
See Also: Validation Set, Training
TinyML
Level: 🔴 Advanced
Definition: Deploying ML models on ultra-low-power microcontrollers, enabling AI on embedded devices.
Context: Useful for battery-powered IoT sensors, wearables, and remote monitoring.
See Also: Edge AI, Federated Learning
Tokenization
Level: 🟢 Beginner
Definition: Splitting text into smaller units (tokens), such as words or subwords, for NLP tasks.
Context: A foundational step in any text processing pipeline.
See Also: NLP Pipeline
Training
Level: 🟢 Beginner
Definition: The process of feeding data into a model and adjusting parameters to minimize a loss function.
Context: Can be computationally intensive, especially for deep networks.
See Also: Inference, Loss Function
Transfer Learning
Level: 🟡 Intermediate
Definition: Using knowledge gained from one task to improve learning on another, related task.
Context: Common in computer vision (pretrained ImageNet models) and NLP (pretrained Transformers).
See Also: Multi-Task Learning, Few-Shot Learning
Transformer
Level: 🔴 Advanced
Definition: A neural network architecture that uses self-attention mechanisms and excels in NLP tasks.
Context: Forms the basis of many state-of-the-art language models (e.g., BERT, GPT).
See Also: LLM, NLP
~ U ~
Underfitting
Level: 🟢 Beginner
Definition: When a model fails to capture underlying trends in the data, leading to low accuracy even on training data.
Context: Can be addressed by using more complex models or training longer.
See Also: Overfitting, Regularization
Unsupervised Learning
Level: 🟢 Beginner
Definition: A learning approach without labeled data, where the model aims to find hidden patterns or groupings.
Context: Clustering and dimensionality reduction are popular unsupervised methods.
See Also: Clustering, GAN
~ V ~
Validation Set
Level: 🟢 Beginner
Definition: A subset of data used during model training to tune hyperparameters and avoid overfitting.
Context: Helps identify optimal model settings before final testing.
See Also: Test Set, Training
Vector Embedding
Level: 🟡 Intermediate
Definition: A numeric representation of data (words, images) in a lower-dimensional space, capturing semantic or structural similarities.
Context: Commonly used in NLP (Word2Vec, GloVe) and recommender systems.
See Also: Tokenization, NLP
~ W ~
Weight
Level: 🟡 Intermediate
Definition: A parameter in a neural network that transforms input data within each neuron’s computation.
Context: Adjusted during backpropagation to minimize loss.
See Also: Backpropagation, Neural Network
Word2Vec
Level: 🟡 Intermediate
Definition: A method for learning vector embeddings of words, capturing semantic and syntactic similarities.
Context: Pioneered large-scale adoption of embedding-based NLP approaches.
See Also: Vector Embedding, NLP
~ X ~
XAI (Explainable AI)
Level: 🔴 Advanced
Definition: Techniques and frameworks aimed at making AI models more transparent and interpretable.
Context: Crucial in regulated industries (finance, healthcare) and for building public trust in AI systems.
See Also: Ethics, Bias
~ Y ~
YOLO (You Only Look Once)
Level: 🟡 Intermediate
Definition: A real-time object detection algorithm known for speed and accuracy in identifying objects in images.
Context: Widely applied in surveillance, autonomous driving, and robotics.
See Also: Computer Vision, Deep Learning
~ Z ~
Zero-Shot Learning
Level: 🔴 Advanced
Definition: A technique enabling models to classify or perform tasks on categories they’ve never been explicitly trained on.
Context: Relies on semantic knowledge transfer; valuable in rapidly changing domains.
See Also: Few-Shot Learning, Transfer Learning
Bookmark This Page
This AI Glossary is designed to be a living resource, adaptable as new technologies and methodologies emerge—especially in the vibrant AI ecosystems across Asia. If you notice a missing term or have an updated definition, let us know in the comments or via our form. Bookmark this page and check back for regular updates!
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Learning
Build Your Own Custom GPT in Under 30 Minutes – Step-by-Step Beginner’s Guide
Create your own GPT without writing code. This beginner-friendly guide shows you how to build, customise, and publish a ChatGPT assistant using OpenAI’s builder — complete with personality, knowledge, and tools.
A friendly guide to building your personalised ChatGPT assistant (custom GPT creation) in under 30 minutes
The Quick Essentials
Before we dive in, here’s what you need to know:
- You’ll need a ChatGPT Plus or Enterprise account (the paid version)
- Creating a custom GPT involves naming it, giving it a personality, uploading knowledge files, and enabling tools
- The entire process happens at chat.openai.com/gpts
- No coding required — just plain English instructions
- You can keep your GPT private, share it via link, or publish it in the GPT Store
Step 1: Getting Access
First things first, you need a ChatGPT Plus subscription:
- Head over to chat.openai.com
- Click “Upgrade to Plus” if you haven’t subscribed yet
- Confirm your plan and payment (USD $20/month at the time of writing)
Once you’re all subscribed:
- Navigate to: chat.openai.com/gpts
- Click the black “Explore GPTs” button in the left menu
- At the top right, click “Create”
Brilliant! You’re now in the GPT Builder interface where all the magic happens.
Step 2: Starting in “Create” Mode
You’ll see a chat interface asking: “What would you like to make?”
This is where you describe your GPT in plain language. For example:
“I’d like to create a friendly GPT that helps junior marketers in the UK write social media copy. It should use casual British English and understand cultural references from across the UK.”
The system will respond with follow-up questions like:
- What tone should it use?
- Should it browse the internet or run code?
- Will you upload any knowledge files?
Answer naturally and conversationally. The system builds a draft GPT based on your answers.
It may even recommend a name and an image:
Once the chat version feels roughly right, click “Configure” in the top bar to manually fine-tune everything.
Step 3: Configuring Your GPT
This is the control panel where you can edit every setting. Follow these steps:
3.1 — Name Your GPT
- In the Name field: give it a clear name like “Brit Copy Buddy”
- In Description: write what it does — “Helps junior UK marketers write scroll-stopping social copy in casual British English.”
3.2 — Write the Instructions
Scroll down to the Instructions box and type your behaviour settings. Think of this like a personality manual. If you followed the steps, then it may well already be filled in for you. This is where the magic happens, so make sure it truly reflects your purpose. Because it is so important, we created a separate guide for this which you can read here. For example:
This GPT is a friendly and culturally aware assistant designed to help junior marketers in the UK write engaging social media copy. It communicates in a casual, approachable tone using British English, including regionally familiar slang and references from across England, Scotland, Wales, and Northern Ireland. It provides clear, supportive guidance to help users improve their writing, offering creative suggestions while keeping brand voice and audience in mind. It can reference UK cultural events, holidays, humour, and idioms to make content feel local and relevant. It avoids Americanisms and ensures that grammar, spelling, and phrasing are aligned with UK standards. The assistant will ask for context when needed (e.g., target audience or platform), and will aim to keep things breezy, witty, and scroll-stopping.
3.3 — Add Conversation Starters
Under Conversation Starters, add 3–4 useful prompts users might click on:
- “Help me write a tweet for a UK skincare brand launch”
- “Can you make this Instagram caption sound more British?”
- “Draft some TikTok captions about a new meal deal”
This helps users jump straight in without typing from scratch.
Step 4: Adding Custom Knowledge (Optional but Recommended)
If you want the GPT to reference your own documents — like brand guidelines or FAQs — follow these steps:
- Scroll to the Knowledge section
- Drag and drop your files (accepted formats: .pdf, .txt, .csv, .md, .json)
- Upload limit is around 20 files at present
Example: Upload a “Tone of Voice Guide.pdf” and your GPT will use it to match your brand style.
Important note: Your GPT can reference but not quote files word-for-word. It learns the content conceptually rather than memorising exact phrases.
We have created a step-by-step guide for the best way to structure your Knowledge files here.
Step 5: Enabling Tools
Now choose which abilities your GPT should have.
Scroll to Capabilities, and toggle the following:
- ✅ Web Browsing — useful for real-time info like news or trends
- ✅ Code Interpreter — for handling files, calculations, data plots
- ✅ Image Generation — if you want it to create pictures (e.g., Instagram ideas)
- ✅ File Uploads — lets users feed the GPT spreadsheets or PDFs
Select only what’s genuinely useful — too many tools can make your GPT’s responses confusing.
Step 6: Testing Your GPT Thoroughly
Before publishing, have a proper chat with your GPT.
Ask both straightforward and unusual questions to test:
- Does it maintain the right tone?
- Does it understand your uploaded files?
- Does it use tools properly (like making charts or browsing)?
- Does it clarify things when uncertain or guess incorrectly?
If anything feels off, go back to the Instructions and tweak your wording. Even changing one line can make a significant difference.
Step 7: Create New Action
This function allows you to turn your GPT into a powerful API-aware assistant that can fetch data, trigger services, or complete tasks — all from inside the chat.
This is an optional extra when creating a Custom GPT and is complicated enough to need its own guide, which you can read here.
If this is your first attempt at creating a Custom GPT, we suggest skipping this step for now and moving on to Step 8.
Step 8: Switch Off Training
You should always consider anything sensitive you share with any AI chatbot. However, its always a good idea to switch off the request to improve the AI models.
Step 9: Publishing Your GPT
At the bottom right, click the “Publish” button.
You’ll be asked to choose:
- Private — only visible to you
- Unlisted — only people with your link can access it
- Public — listed on the GPT Store for anyone to use
Give it a thumbnail image (upload one or use the auto-generated option), choose a category (e.g., Marketing, Productivity), and confirm.
Congratulations! Your GPT is now live and you can view it.
Final Tips for Success
- 🧹 Keep it focused — One GPT = one clear purpose
- 🪪 Use your brand voice — match the tone your users expect
- 🔁 Iterate regularly — update your files and instructions as you learn what works
- 💬 Share wisely — use private/unlisted first before going public
Happy GPT creating! With these steps, you’ll be up and running with your custom assistant in no time.
You may also find useful:
- How to Upload Knowledge into Your Custom GPT
- How to Use the “Create an Action” Feature in Custom GPTs
- Or try this playbook out now at ChatGPT by tapping here.
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How to Upload Knowledge into Your Custom GPT
Want your Custom GPT to actually know your stuff? Learn how to upload your own documents into ChatGPT’s Knowledge section, with step-by-step instructions, supported file formats, and key watchouts.
Your step-by-step guide to making a smarter GPT with your own documents.
Why Upload Your Own Knowledge?
Even the best GPT won’t really sound like you — or know your stuff — unless you teach it. That’s where knowledge uploads come in.
By uploading files (PDFs, DOCs, TXT, etc.), your GPT can:
- Answer questions based on your business material
- Speak in your tone and style
- Cut down on repetitive manual responses
- Act as a trained assistant, contract reviewer, customer explainer, or internal helpdesk
Think of it as giving your GPT “homework.” You hand it your documents, and it quietly studies them behind the scenes so it can sound smart in front of your users.
Why Upload Your Own Knowledge?
Even the best GPT won’t truly sound like you — or know your stuff — unless you teach it. That’s where knowledge uploads come in.
By uploading files (PDFs, DOCs, TXT, etc.), your GPT can:
- Answer questions based on your business material
- Speak in your tone and style
- Cut down on repetitive manual responses
- Act as a trained assistant, contract reviewer, customer explainer, or internal helpdesk
Think of it as giving your GPT “homework.” You hand it your documents, and it quietly studies them behind the scenes so it can sound smart in front of your users.
Step-by-Step: How to Upload Documents to a Custom GPT
Prerequisite: You’ve already created your Custom GPT (via https://chat.openai.com/gpts). You’re now ready to add your own knowledge base by uploading documents.
Step 1: Go to the GPT Builder
Go to https://chat.openai.com/gpts. Click on your Custom GPT and select “Edit GPT.”
Step 2: Find the “Knowledge” Section
In the left-hand menu, select “Knowledge” and click “Upload files.”
Step 3: Add Your Files
Drag and drop or browse to upload your documents. Supported formats include .pdf, .docx, .txt, .md, .csv. You can upload up to 20 files with a combined size of 512 MB. You can ask ChatGPT to help you assess and convert documents into these formats if you need to with this prompt:
I want to use this document inside a Custom GPT as part of its Knowledge section. Please assess the content and do the following:
Identify if this content is suitable to be uploaded directly (e.g. clear, clean, complete), or if it needs to be rewritten, summarised, or broken into smaller chunks.
If the formatting is poor (e.g. tables, layout issues, scanned PDF style), convert it into clean, text-based markdown or plain text format that preserves all meaning and structure.
Remove any unnecessary elements such as headers/footers, page numbers, duplicated content, or visual formatting that won’t translate well into plain text.
Structure the output into a clean, well-labelled text file that can be uploaded into the Knowledge section of a Custom GPT (i.e. .txt or .md format). Use clear section titles and bullet points where appropriate.
Keep all the important content, but make sure it’s optimised for retrieval by a GPT model. That means using simple, clear language and logical structure.
Name the output file appropriately (e.g. “2025_PricingOverview.txt” or “Legal_Terms_Guide.md”).
Please begin by assessing the suitability of the input and then output a clean, upload-ready version.
[Optional Tip (if you’re uploading a file):
Start with:]
“Please assess the uploaded file using the instructions below…” and paste the prompt afterward.
Step 4: Check the File List
You’ll see a list of your uploaded files. Use the trash icon to remove any if needed. You can update this list at any time.
Step 5: Save and Publish
Click “Save” or “Publish” to apply your changes. Your GPT can now access your uploaded documents to answer relevant prompts.
What Kind of Files Work Best?
Ideal Files:
- Cleanly written PDFs (guides, SOPs, FAQs)
- Contracts and legal templates
- Onboarding documents, pricing sheets
- Internal wikis (exported to .txt or .md)
Avoid These:
- Scanned documents with images
- Slides with only images or no speaker notes
- Encrypted or locked PDFs
- Files full of links without explanations
Tip: For web pages, copy-paste the content into a clean .txt or .md file.
How the GPT Uses This Info
Your GPT will search the uploaded documents in real time when a relevant prompt is given. It doesn’t memorise the content — it retrieves from it. It performs best when the material is clearly written and structured.
Watch Outs
- No File Structuring = Confused GPT
If you upload a single giant PDF with 50 topics and poor formatting, the GPT will struggle. Break it into smaller, well-labelled files. - Bad Formatting = Bad Responses
If your file has unusual fonts, broken tables, or visual layouts (especially common in PDFs), the GPT may misread it. Clean formats like .txt, .docx, or markdown work best. - No Source Citations
By default, GPT won’t say where the information came from. If this matters, add an instruction like: “Always mention which document you’re referencing.” - File Limit
You can only upload 20 files per GPT. Curate carefully and consider trimming or combining related documents.
Curating the “Core Knowledge” for Best Results
Ask yourself:
- What do I want this GPT to do? Only upload documents relevant to those tasks.
- Will someone else use this? Include glossaries or context if needed.
- Is this content clear and self-contained? If not, simplify or split into manageable chunks.
Example Use Cases:
LegalGPT: Upload contracts, clause trackers, fallback templates
SalesGPT: Upload pitch decks, product specs, objection-handling guides
HRGPT: Upload company policies, onboarding FAQs
Bonus Tip: Pair With System Instructions
After uploading, adjust your GPT’s instructions to reflect how it should use that knowledge. Example: “You are a helpful assistant trained specifically on SQREEM’s legal contracts and internal SOPs. Always answer using information from the uploaded documents. If unsure, say ‘I’m not certain — please check with legal.’” You can also use the “Prompt Starter” section to load reusable queries.
Updating Your Knowledge Files Later
Return to “Edit GPT > Knowledge” anytime to remove outdated files or upload new ones. Save to apply changes. Your GPT will instantly use the latest content.
Version Control and Multiple GPTs
You can create multiple GPTs with different document sets, or use file naming conventions to stay organised. Examples:
01_PricingOverview_Q1-2025.txt
02_TOS_Updated_April2025.docx
03_FAQ_InternalOnly.md
What To Do Next
Now that your GPT has your content, test it by asking:
“Summarise our latest pricing model”
“What’s our refund policy?”
“Write a client email using our onboarding flow”
“Check clause 7.3 in the uploaded SOW template”
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How to Use the “Create an Action” Feature in Custom GPTs
This guide walks through the Create an Action feature in OpenAI’s GPT builder — enabling your GPT to call live APIs for real-time data or actions. Written for Asia’s business and tech professionals, it shows how to connect internal tools to GPTs in just a few clear steps.
Turn your GPT into a powerful API-aware assistant that can fetch data, trigger services, or complete tasks — all from inside the chat.
TL;DR — What You Need To Know
- “Actions” let your custom GPT interact with external APIs — think booking systems, CRMs, weather lookups or internal databases
- You define the API, describe it with an OpenAPI schema, and explain when and how the GPT should use it
- Ideal for businesses that want to automate workflows inside ChatGPT — without needing full app builds
- Common use cases across Asia include: order lookups, stock checks, HR systems, and appointment scheduling
- This guide walks you through building, testing, and deploying your first Action step by step
What Is a GPT Action?
Actions allow your GPT to call an external API endpoint during a conversation. For example:
“Can you check the current air quality in Jakarta?”
→ GPT sends a request to an API you defined
→ Returns real-time AQI data into the chat
This means your GPT isn’t just a static assistant — it becomes a live, interactive tool that can “do” things.
Step 1: Set Up Your Custom GPT
Start as usual:
- Go to: https://chat.openai.com/gpts
- Click “Create”
- Fill out your GPT’s name, instructions, and tone as needed
- Switch to the Configure tab
Step 2: Prepare Your API
You’ll need an existing web API to connect. This could be:
- A public API (like OpenWeather, Google Calendar, etc.)
- A private internal API (e.g., inventory, HR systems, internal bots)
- A no-code tool like Zapier or Make, which exposes endpoints
Make sure:
- It supports HTTPS
- It accepts and returns JSON
- You have the API key (if it requires auth)
Example Use Case:
Check a user’s leave balance via an HR API.
Step 3: Write Your OpenAPI Schema
This is how GPT knows what your API does. You describe it using the OpenAPI 3.0 format — a structured YAML or JSON file.
Here’s a simple example for a GET request to check leave days:
yamlCopyEditopenapi: 3.0.0
info:
title: HR API
version: 1.0.0
paths:
/leave-balance:
get:
summary: Check user's leave balance
parameters:
- in: query
name: employee_id
schema:
type: string
required: true
description: ID of the employee
responses:
'200':
description: Leave balance returned
content:
application/json:
schema:
type: object
properties:
remaining_days:
type: integer
Paste this into an .yaml file or host it on a public URL (e.g., via GitHub Gist or a secure S3 bucket).
Step 4: Add the Action in GPT Builder
Back in the Configure tab:
- Scroll to “Actions”
- Click “Add Action”
- Paste your API’s base URL
- Paste or link to your OpenAPI schema
- Add a description for GPT — e.g., “Use this action to check leave balances when users ask about time off”
- Set authentication:
- No auth
- API key (via headers or query param)
- OAuth (advanced)
Once added, test the schema using GPT’s built-in validator.
Step 5: Write GPT Instructions to Use the Action
Update your GPT’s Instructions to explicitly describe when to use the action.
Example:
When a user asks about leave days, use the HR API to check their balance. Ask for their employee ID first. Do not guess.
This ensures GPT won’t try to call the API unless it’s appropriate.
Step 6: Test the Action
Switch to Preview GPT and type:
“How many leave days do I have left?”
→ GPT should ask for your employee ID
→ Then call the action
→ Then return the result, e.g. “You have 12 leave days remaining.”
Watch for:
- Correct input formatting
- Unexpected errors or failed calls
- GPT failing to use the action when it should
Fix instructions or the schema if anything breaks.
Step 7: Publish and Maintain
Once you’re confident:
- Hit Publish
- Choose Private, Link or Public visibility
- Keep your API uptime in mind — if the endpoint is down, GPT won’t function properly
- Monitor logs and rate limits if it’s a high-traffic GPT
Real-World Examples from Asia
- Singapore travel agencies integrating visa APIs for instant eligibility checks
- Malaysian e-commerce startups checking stock or delivery status via GPT
- Indonesian HR tech firms adding internal policy lookups via GPTs
- Thai insurance brokers offering premium calculators through live API calls
The best part? You can connect more than one action — allowing your GPT to call several services like a digital command centre.
Security Note
Always hide API keys and use headers or environment variables for private services. GPTs do not store user data, but you are responsible for how your API handles it.
Avoid exposing endpoints with write access (like deleting records or submitting payments) unless fully secured and monitored.
Final Thoughts: ChatGPT as Your API Concierge
With Actions, GPTs are no longer just helpful assistants — they’re functional bridges into your real systems. In Asia, where lean automation is everything, this may be the most powerful GPT feature yet.
Whether you’re triggering a report, checking a shipment, or logging a support ticket — if there’s an API for it, GPT can now do it.
So the question becomes: what internal service would you automate first?
You May Also Like:
- Build Your Own Agentic AI — No Coding Required
- Or tap here to try this now at ChatGPT.
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