docs: update glossary

Author: sinedied

glossary.md

@@ -5,25 +5,30 @@ A comprehensive list of technical terms used throughout the lessons.
 - **API**: 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.
-- **Braided Search**: Combines algorithmic search with human-provided examples to find more contextually relevant results in AI 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**: 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.
 - **Interactive Development Environments (IDEs)**: Software providing coding, debugging, and testing tools for developers.
 - **JSON**: 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.
 - **Large Language Model (LLM)**: 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.
 - **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.