Artificial Intelligence: The ability of machines to simulate human intelligence and perform tasks like image recognition, text understanding, and problem solving.

Machine Learning: A branch of AI that enables systems to learn from data and improve performance without explicit programming.

A type of ML using multi-layer neural networks to learn complex patterns.

Large Language Model: An AI model trained on massive text datasets that can understand and generate human language.

The text input given to an AI model to guide it toward a specific task or response.

A unit of text processed by AI models, roughly equivalent to 4 characters or ¾ of a word.

Mathematical representation of text, images, or data in vector space, enabling AI models to understand semantic similarity.

Specialized database for efficiently storing and searching embeddings and vectors.

Retrieval-Augmented Generation: Technique connecting AI model to external knowledge base to improve accuracy and reduce hallucination.

An AI system capable of performing multiple tasks autonomously, using tools and making decisions to achieve a goal.

Application Programming Interface: A set of protocols that allow different programs to communicate and exchange data.

Organized collection of data used for training, testing, or evaluating AI models.

A mathematical program trained on data to perform a specific task like classification, generation, or prediction.

The process of teaching an AI model by feeding large amounts of data and adjusting parameters to improve performance.

Using a trained AI model to get predictions or responses from new inputs.

Training a pre-trained model on specialized data to make it more efficient in a specific domain.

Internet-based computing infrastructure providing processing, storage, and networking capabilities on demand.

Platform that containerizes applications ensuring they run the same way in any environment.

System for automating deployment, management, and scaling of containerized applications.

Set of practices combining ML development with operations to deploy and maintain models in production.

Development practices automating code testing and deployment on every change.

Graphics Processing Unit: Specialized processor for parallel computations, essential for training AI models.

The ability of an AI model to call external functions or APIs to get information or execute tasks.

Model Context Protocol: Open standard from Anthropic allowing Claude to interact with external systems and tools.

CLI tool from Anthropic enabling Claude to work as a full coding agent in the terminal.

Open-source Firebase alternative providing Postgres, Auth, Storage, and Edge Functions.

Cloud deployment platform specialized in Next.js and frontend applications.

Code hosting platform built on Git, used for collaboration and managing programming projects.

Mechanism allowing one application to notify another of an event by sending an automatic HTTP request.

Using technology to perform tasks automatically without human intervention.

Security attack where an attacker attempts to override system instructions for an AI model to make it behave unintentionally.

When an AI model generates information that appears correct but is actually false or fabricated.

The maximum amount of text an AI model can process at one time.

Parameter that controls how random and creative AI model responses are.

Hidden instructions given to an AI model before conversation to define its personality, behavior, and constraints.

Secret authentication token used to access an API service.

Value stored outside the code used to securely store settings and secrets.

Execution model allowing code to run without managing servers, paying only for what you use.

Code that runs on servers geographically close to the user to reduce latency.

Search technique finding results based on semantic similarity rather than literal matching.

Revolutionary neural network architecture using self-attention, the architectural foundation of all modern LLM models.

A model's ability to understand and process different types of data simultaneously such as text, images, and audio.

Reinforcement Learning from Human Feedback: training technique improving models based on human ratings of response quality.

Asking an AI model to perform a task directly without providing any prior examples of the expected output.

Providing a few examples of the desired output within the prompt to guide the model toward the expected format.

JavaScript Object Notation: lightweight, human-readable data exchange format widely used in APIs and web applications.

Architectural pattern for system communication via HTTP using GET/POST/PUT/DELETE verbs to manage resources.

Software whose source code is publicly available for anyone to read, modify, and distribute freely.