AI & Programming Glossary

An iterative project management methodology that emphasizes flexibility, collaboration, and delivering working software in short cycles (sprints). Popular frameworks: Scrum, Kanban.

An autonomous AI system that can plan, execute multi-step tasks, use tools, and make decisions to achieve a goal — with minimal human intervention.

A step-by-step set of instructions for solving a problem or completing a task. Algorithms are the foundation of all computer programs and are measured by their efficiency and correctness.

A set of rules that allows different software applications to communicate with each other. APIs define how requests and responses should be formatted, enabling integration between services.

The simulation of human intelligence by machines, enabling them to learn, reason, and make decisions. AI powers chatbots, recommendation systems, self-driving cars, and more.

The server-side of a web application — databases, APIs, authentication, and business logic. Everything that happens behind the scenes when a user interacts with a website or app.

An error, flaw, or unexpected behavior in a software program. Bugs can cause crashes, incorrect results, or security vulnerabilities. Debugging is the process of finding and fixing them.

A distributed network of servers that delivers web content to users from the nearest geographic location. CDNs dramatically improve load times, reduce latency, and handle traffic spikes.

Automated practices that integrate code changes, run tests, and deploy applications. CI ensures code is tested on every commit, while CD automates the release process to production.

Delivering computing services — servers, storage, databases, networking — over the internet instead of on local machines. Major providers: AWS, Google Cloud, Microsoft Azure.

A field of AI that enables machines to interpret and understand visual information from images and videos. Used in facial recognition, medical imaging, and quality inspection.

An organized collection of structured data stored electronically. Types include relational (SQL like PostgreSQL) and non-relational (NoSQL like MongoDB). Essential for every application.

A type of machine learning using neural networks with many layers. Powers advanced tasks like language translation, voice assistants, and autonomous driving.

A set of practices combining software development (Dev) and IT operations (Ops) to shorten the development lifecycle. DevOps emphasizes automation, monitoring, and continuous delivery.

A platform for packaging applications and their dependencies into portable containers. Containers run consistently across any environment, eliminating 'it works on my machine' problems.

A numerical representation of text, images, or data as vectors in a high-dimensional space. Embeddings capture semantic meaning, enabling similarity search and comparisons.

The process of further training a pre-trained AI model on a specific dataset to adapt it for a particular task or domain, improving its performance on targeted use cases.

Google's UI toolkit for building natively compiled mobile, web, and desktop applications from a single Dart codebase. Known for fast development, expressive UI, and native performance.

A pre-built collection of tools, libraries, and conventions that provides a structure for developing applications. Examples: React, Next.js, Django, Flutter.

The part of a website or app that users see and interact with directly. Built with HTML, CSS, and JavaScript — often using frameworks like React, Vue, or Angular.

A developer or application that covers both frontend and backend development. Full-stack developers can build complete web applications from the user interface to the database.

AI systems that create new content — text, images, code, music, or video — based on patterns learned from training data. Examples: ChatGPT, Midjourney, GitHub Copilot.

A distributed version control system that tracks changes in source code during development. Git enables multiple developers to collaborate on the same project simultaneously.

When an AI model generates information that sounds plausible but is factually incorrect or fabricated. A key challenge in deploying AI for critical applications.

The process of using a trained AI model to make predictions or generate outputs on new, unseen data. This is what happens when you interact with ChatGPT or any deployed AI system.

An AI model trained on massive text data to understand and generate human-like text. Examples include GPT, Claude, and Gemini.

A collection of pre-written code that developers can reuse to perform common tasks. Unlike frameworks, libraries are tools you call when needed. Examples: Lodash, Tailwind CSS.

A subset of AI where systems learn patterns from data without being explicitly programmed. Used in spam filters, image recognition, and predictive analytics.

An open protocol by Anthropic that standardizes how AI models connect to external tools, data sources, and services. MCP lets any AI assistant securely access files, databases, APIs, and other resources through a universal interface — similar to a USB-C port for AI.

An architecture where an application is built as a collection of small, independent services. Each service handles a specific function and communicates via APIs, enabling scalability and independent deployment.

The branch of AI that helps machines understand, interpret, and generate human language. Powers chatbots, translation, and sentiment analysis.

A computing system inspired by the human brain, made of interconnected nodes (neurons) organized in layers. It's the foundation of deep learning models.

A React framework by Vercel that adds server-side rendering, static generation, API routes, and more. Next.js enables building production-ready web applications with excellent performance and SEO.

A JavaScript runtime built on Chrome's V8 engine that executes JavaScript on the server side. Node.js enables building fast, scalable backend services and APIs using JavaScript.

Software whose source code is publicly available for anyone to view, modify, and distribute. Popular open-source projects include Linux, React, Python, and VS Code.

The skill of crafting effective instructions (prompts) for AI models to get accurate and useful outputs. It involves understanding how models interpret language and context.

A high-level, general-purpose programming language known for its clean syntax and versatility. Widely used in AI/ML, web development, data science, automation, and scripting.

A technique that combines AI generation with information retrieval. The model fetches relevant documents from a knowledge base before generating a response, improving accuracy and reducing hallucinations.

A JavaScript library by Meta for building interactive user interfaces using reusable components. React uses a virtual DOM for efficient updates and is the most popular frontend library.

A training method where an AI agent learns by trial and error, receiving rewards for correct actions and penalties for wrong ones. Used in robotics, game AI, and recommendation systems.

A web design approach that makes websites automatically adapt to different screen sizes and devices. Uses flexible grids, images, and CSS media queries for optimal viewing on any device.

A standard way of building web APIs using HTTP methods (GET, POST, PUT, DELETE). RESTful APIs are stateless, scalable, and the most common pattern for web services communication.

The practice of optimizing a website to rank higher in search engine results. Includes technical optimization, content strategy, keyword targeting, and building quality backlinks.

Rendering web pages on the server before sending them to the browser. SSR improves initial load performance and SEO because search engines can index fully rendered content.

Security protocols that encrypt data transmitted between a web browser and server. SSL/TLS certificates enable HTTPS, ensuring data privacy, integrity, and authentication.

The basic unit of text that AI models process. A token can be a word, part of a word, or a character. Models charge based on the number of tokens processed.

The dataset used to teach an AI model. The quality, quantity, and diversity of training data directly affect the model's performance and accuracy.

The neural network architecture behind modern LLMs. It uses self-attention mechanisms to process entire sequences of text simultaneously, enabling better context understanding.

A superset of JavaScript that adds static type checking. TypeScript catches errors at compile time instead of runtime, making code more reliable and maintainable in large projects.

A system that records changes to files over time, allowing developers to recall previous versions, track modifications, and collaborate. Git is the most widely used version control system.

An HTTP callback that sends real-time data to a URL when a specific event occurs. Webhooks enable automated notifications between applications without constant polling.