# The AI Dictionary

A quick cheat-sheet for all the terms you will hear when talking about prompts, models, and artificial intelligence.

## The Basics

- **Large Language Model (LLM)**: An AI model trained on massive amounts of text to understand and generate human language.
- **Artificial Intelligence (AI)**: Computer systems designed to perform tasks that normally require human intelligence.
- **Prompt**: The instruction or input given to an AI model.
- **Prompt Engineering**: The practice of designing prompts to get better and more reliable AI outputs.
- **Context**: Additional information that helps the model better understand the task or situation.
- **Token**: A small unit of text processed by the model, such as a word or part of a word.
- **Hallucination**: A false or invented answer generated by the AI that sounds believable.
- **Chain of Thought**: A reasoning style where the model explains intermediate thinking steps.
- **Multimodal Model**: An AI model capable of processing multiple data types such as text, images, and audio.
- **Agent**: An AI system that can autonomously perform actions and use tools to complete tasks.
- **Tool Calling**: The ability of an AI model to use external tools, APIs, or services during a task.

## Under the hood

- **System Prompt**: Hidden instructions that define the AI's behavior, rules, and personality.
- **Context Window**: The maximum amount of text an AI model can process and remember at once.
- **Context Injection**: Adding external information into a prompt to improve the model's response.
- **Token Limit**: The maximum number of tokens a model can handle in a single interaction.
- **Training Data**: The large collection of text and information used to train the model.
- **Inference**: The process of generating an output using a trained AI model.
- **Completion**: The text response generated by the AI model.
- **Next-Token Prediction**: The core mechanism where the model predicts the most likely next token in a sequence.
- **Transformer**: The neural network architecture used by modern language models like GPT.
- **Attention Mechanism**: A system that helps the model focus on the most relevant parts of the input.
- **Weights**: The learned internal parameters that store the model's knowledge.
- **Temperature**: A setting that controls how creative or predictable the model's responses are.
- **Reasoning Model**: A model optimized for deeper logical thinking and step-by-step problem solving.
- **Hallucination Rate**: The frequency at which a model produces inaccurate or fabricated information.
- **Grounding**: Connecting AI outputs to real data or trusted sources to improve accuracy.
- **Alignment**: The process of making AI systems follow human goals, values, and safety rules.
- **Guardrails**: Safety restrictions designed to prevent harmful or unwanted AI behavior.
- **Jailbreak**: An attempt to bypass the AI model's built-in restrictions or safety systems.
- **Few-Shot Prompting**: Providing a few examples in the prompt to guide the model's behavior.
- **One-Shot Prompting**: Providing a single example in the prompt to demonstrate the desired output.
- **Zero-Shot Prompting**: Asking the model to perform a task without giving examples.
- **Prompt Chaining**: Breaking a complex task into multiple connected prompts.
- **Structured Output**: A response generated in a fixed format such as JSON, XML, or tables.
- **Few-Shot Learning**: The ability of a model to adapt to tasks using only a small number of examples.
- **Embeddings**: Numerical vector representations of text used to capture semantic meaning.
- **Vector Database**: A database optimized for storing and searching embeddings.
- **Semantic Search**: Search based on meaning and context instead of exact keywords.
- **Retrieval-Augmented Generation (RAG)**: A method where the model retrieves external information before generating a response.
- **Fine-Tuning**: Additional training of a model on specialized data for a specific task or domain.

[Prompt guide](https://prompt-coach.pro/en/guide)

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