🤖 AI Terminology Glossary
With simple explainers
Agentic
Simple Meaning: Describes an AI that can plan, act, and course-correct on its own to achieve a complex goal. An AI with a degree of autonomy.
Chunking
Simple Meaning: The process of breaking down a large piece of text or data into smaller, manageable, and contextually relevant segments before feeding them into an AI model.
Deep Learning (DL)
Simple Meaning: A more advanced form of Machine Learning that uses neural networks with many layers (deep networks) to analyze complex data like images, sound, and text.
Generative AI
Simple Meaning: AI that can create new content, such as text, images, code, or music, rather than just classifying or analyzing existing data.
Hallucination
Simple Meaning: A term for when a Generative AI model invents facts or produces confidently stated information that is false, misleading, or nonsensical.
Inference
Simple Meaning: The process of using a trained AI model to make a prediction or arrive at a decision based on new, unseen data.
Large Language Model (LLM)
Simple Meaning: An AI model trained on massive amounts of text data to understand, summarize, translate, and generate human-like text.
Machine Learning (ML)
Simple Meaning: A type of AI where computers learn from data without being explicitly programmed.
Model
Simple Meaning: The core output of the AI training process. It's a file containing all the learned patterns, rules, and knowledge that the AI uses to make predictions or generate content.
Neural Network
Simple Meaning: A computational system inspired by the structure and function of the human brain. It consists of interconnected layers of "nodes" (neurons) that process information.
Observability
Simple Meaning: The ability to understand what is happening inside an AI system—why it made a specific decision, how it's performing, and if it's running efficiently.
Orchestration
Simple Meaning: The automated management and coordination of multiple AI models, tools, and data flows to work together as a single, seamless system.
Parameters
Simple Meaning: The learned variables or weights inside an AI model that are adjusted during training. These numbers essentially store the model's knowledge.
Prompt Engineering
Simple Meaning: The art and science of writing effective instructions or queries (prompts) to get the best and most accurate results from a generative AI model.
Self-Learning
Simple Meaning: A broad term describing an AI system that can improve its own performance or adapt its behavior over time without direct human intervention or continuous labeled data.
Supervised Learning
Simple Meaning: A type of Machine Learning where the model is trained using labeled data. Every input is paired with the correct output.
Synthetic Data
Simple Meaning: Any data that is artificially generated rather than being collected from real-world events. It is created using algorithms.
Training
Simple Meaning: The process of feeding data to an AI model so it can learn and adjust its internal settings to perform a specific task.
Vector Database
Simple Meaning: A specialized database designed to efficiently store and retrieve information based on meaning and context rather than keywords.
Transformer
Simple Meaning: A type of neural network architecture that revolutionized Large Language Models (LLMs) by allowing the model to weigh the importance of different parts of the input data (text) when processing it.
Key Feature: The Transformer introduced the attention mechanism, which enables models to understand long-range dependencies in text, making them far more effective at complex language tasks.
Ontology
Simple Meaning: In AI and computer science, an ontology is a formal, explicit specification of a shared conceptualization. Essentially, it defines a set of concepts, categories, properties, and relationships that exist for a domain of discourse.
Analogy: Think of it as a detailed, structured map of knowledge for a specific area (like "healthcare" or "finance"). It ensures that all AI models and systems operating in that domain have a consistent understanding of the terminology and how the concepts are connected.
Application: Helps AI models perform more accurate knowledge reasoning and retrieval, as they aren't guessing the meaning of terms.
Retrieval-Augmented Generation (RAG)
Simple Meaning: A technique that combines the power of an LLM with external knowledge search (retrieval). Before generating an answer, the system first searches a private or proprietary database for relevant information.
Business Value: RAG reduces "hallucination" and ensures the AI's response is grounded in specific, up-to-date, and internal data, making the output accurate and relevant to a company's unique context. It allows LLMs to use knowledge they were not trained on.
Small Language Model (SLM)
Simple Meaning: An AI model with a small number of parameters (typically millions to a few billion). It is designed to be computationally efficient and run quickly on devices with limited resources, like smartphones or embedded hardware.
Trade-off: Offers faster speed and lower cost than Large Language Models (LLMs), but may have less general knowledge and lower performance on highly complex, open-ended tasks.
Medium-Sized Model
Simple Meaning: An AI model that strikes a balance between performance and efficiency. It is larger than an SLM but smaller than the largest LLMs.
Role: Suitable for a wide range of general applications where high accuracy is needed, but the extreme resource cost of the largest models is prohibitive.
Narrow Language Model
Simple Meaning: A model that is specialized or fine-tuned to perform well on a specific set of tasks or within a single domain (e.g., legal, medical, or customer service for one product line).
Business Value: It offers deeper expertise and often higher accuracy than a general model when dealing with domain-specific language and context. A model can be both small and narrow, combining efficiency with specialization.
AI Slop
Simple Meaning: A pejorative term for low-effort, low-quality, mass-produced digital content (text, images, or video) generated by AI, which is perceived to lack human insight, value, or deeper meaning.
Key Characteristic: It prioritizes speed and quantity over substance and quality, often resembling digital clutter or spam created mainly for monetization or cheap engagement.
Business Value Takeaway: To be a thought leader, content must be curated and edited for unique insights, not just generated quickly. High-value content is the opposite of AI slop.
Fine-Tuning
Simple Meaning: The process of taking a pre-trained model (like an LLM) and training it further on a smaller, specific dataset to make it an expert in a particular task or domain.
Multimodality
Simple Meaning: The ability of an AI system to process, understand, and generate information from multiple types of data simultaneously, such as text, images, and audio.
Prompt Injection
Simple Meaning: A type of security attack where a user bypasses the model's safety or system instructions by including a malicious instruction in their prompt.
Reinforcement Learning
Simple Meaning: A type of Machine Learning where an AI agent learns to make a sequence of decisions by interacting with an environment, receiving rewards for good actions and penalties for bad ones (learning by trial and error).
Token
Simple Meaning: The basic unit of text that an LLM uses to process information. Tokens can be whole words, parts of words, or punctuation. The model reads and generates text one token at a time.
Unsupervised Learning
Simple Meaning: A type of Machine Learning where the model is given unlabeled data and must find hidden patterns, structures, or relationships in the data on its own (e.g., grouping customers into categories).
Bias (Algorithmic Bias)
Simple Meaning: Systematic and unfair prejudice in an AI system's results, often due to flaws or imbalances in the training data. The AI reflects and amplifies the biases present in the data it learned from.
Threat: Leads to unequal or harmful outcomes for certain groups, for example, in loan approvals or hiring decisions.
Drift (Model Drift)
Simple Meaning: The phenomenon where the performance or accuracy of a deployed AI model decreases over time because the real-world data it receives (the input) starts to change and deviate significantly from the data it was originally trained on.
Threat: A model that was once highly accurate becomes unreliable or makes irrelevant decisions without warning.
Explainable AI (XAI)
Simple Meaning: A set of methods and techniques that allow humans to understand and trust the results created by machine learning algorithms. It answers the question, "Why did the AI make that decision?"
Mitigation: By providing transparency, XAI helps identify and fix threats like bias and lack of fairness.
Security (AI Security)
Simple Meaning: The practice of protecting AI models and the data they use from malicious attacks, such as prompt injection, data poisoning (tampering with training data), or adversarial attacks (subtly altering input to cause errors).
Threat: Attacks can compromise data integrity, system reliability, and the confidentiality of information.
PyTorch
Simple Meaning: A widely used, open-source machine learning framework developed by Meta AI. It provides a flexible and efficient platform for building and training deep learning models.
Key Feature: PyTorch is known for its dynamic computation graph (allowing for easier experimentation and debugging) and is a fundamental tool for researchers and developers in the AI industry.
Grounding
Simple Meaning: The concept of ensuring an AI model's output is accurate and logically connected to verifiable real-world facts or data sources, rather than relying solely on the patterns learned during training.
Goal: To make the AI's response trustworthy and auditable. In Large Language Models (LLMs), Grounding is often achieved through techniques like RAG (Retrieval-Augmented Generation), where the model retrieves information from a specific database before forming its answer.
