Skip to content

glossary.md

Latest commit

 

History

History
51 lines (46 loc) · 5.36 KB

glossary.md

File metadata and controls

51 lines (46 loc) · 5.36 KB

Glossary

A comprehensive list of technical terms used throughout the lessons.

  • API (Application Programming Interface): A set of rules enabling software applications to communicate with each other, commonly used in generative AI integration.
  • API key: A private key used to authenticate requests to an application programming interface.
  • Augmented Prompt: A prompt enhanced with additional context or information to improve the relevance of AI-generated responses.
  • Azure AI Studio: A platform to build, evaluate, and deploy AI models using Microsoft Azure.
  • Azure OpenAI: A cloud service for deploying and scaling OpenAI models like GPT for applications.
  • Caesar cipher: A substitution cipher shifting characters by a fixed number of places in the alphabet.
  • Chain-of-Thought Prompting: A technique guiding models to break down complex tasks into sequential reasoning steps for better accuracies in outputs.
  • Chatbot: An application designed to simulate conversation with human users, often using natural language processing.
  • Completions API: API to generate text or code based on inputs, used for predictive or generative tasks in AI models.
  • Context Window: The amount of past input that a language model can consider when generating responses, measured in tokens.
  • CSV (Comma-Separated Values): A data format consisting of values separated by commas, often used for structured data retrieval and modification.
  • Embedding: Numeric vector representation of data, often used for semantic search or clustering in machine learning.
  • Escape Hatch: A technique instructing AI to admit lack of knowledge when data is insufficient to ensure accurate responses.
  • Few-Shot Prompting: A method of providing minimal examples to the model to influence its output with specific context or format.
  • Full-Stack Development: Development of both the client (frontend) and server (backend) in software applications.
  • Function Calling: A method for passing structured prompt data into specific functions within an application programmatically.
  • GitHub Codespaces: A cloud-based environment for coding, testing, and running applications directly from GitHub repositories.
  • GitHub Models: A platform hosting pre-trained AI models for use and integration with GitHub development workflows.
  • GitHub Token: An authentication method to access GitHub-hosted APIs or services securely.
  • IDE (Interactive Development Environment): Software providing coding, debugging, and testing tools for developers.
  • JSON (JavaScript Object Notation): A lightweight data-interchange format used for structured information exchange between systems, including generative AI responses.
  • Knowledge Bases: Data repositories used to enhance AI applications by providing reliable, domain-specific information.
  • LangChain: A framework for building AI applications that focus on chaining multiple models and functionalities together.
  • LLM (Large Language Model): AI models trained on large text datasets to generate human-like responses for diverse applications.
  • Maieutic Prompting: A technique involving follow-up queries to challenge or validate AI-generated responses for accuracy and reasoning.
  • Managed Identity: A secure cloud mechanism that provides applications with automatic authentication to access resources without managing passwords.
  • Markdown: A lightweight markup language for formatting plain text into structured layouts, like tables or lists.
  • MCP (Model Context Protocol): A protocol to decentralize applications by separating server capabilities and connection protocols.
  • Meta Prompts: Instructions added before a user's prompt to refine or restrict the AI's behavior and output format.
  • Multimodal Capabilities: AI functionality to process various formats like text, image, or video input and deliver diverse outputs.
  • Node.js: A runtime environment allowing developers to execute JavaScript code server-side for building scalable applications.
  • OpenAI: A pioneering organization in AI research and APIs for language models integrated into applications for generative tasks.
  • Prompt Engineering: The process of crafting effective prompts to guide AI models toward desired responses and behaviors.
  • RAG (Retrieval-Augmented Generation): A technique combining retrieval-based methods with generative models for more accurate, data-grounded outputs.
  • Semantic Search: Search method leveraging the meaning of terms for more contextually accurate and nuanced results.
  • Structured Output: Data output organized in predefined formats like tables or JSON, enabling easier integration with systems.
  • System Message: A prompt in conversational AI that specifies contextual boundaries or personality for the assistant.
  • TensorFlow.js: A JavaScript-based machine learning library enabling browser and Node.js-based AI/ML applications and training.
  • Tokenizer: A tool used to convert text into tokens, providing structure for how data is inputted or analyzed by models.
  • Vector Search: Retrieval technique comparing encoded vectors to find semantically similar information in AI applications.
  • XML (eXtensible Markup Language): A markup language formatting structured data for information storage, exchange, or generative model input/output.

Technical Terms

The following terms should NOT be translated:

  • prompt