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AI & Prompt Engineering Glossary
Plain-English definitions of the AI and prompt-engineering terms professionals actually run into — what they mean, when they matter, and how to use them. Every term links to real, copy-paste prompts in the free Prompt Library.
AI Hallucination
A hallucination is when an AI states something false or fabricated — a fake citation, statistic, or quote — with total confidence.
Chain-of-Thought Prompting
Chain-of-thought prompting asks the model to reason step by step before answering, improving accuracy on multi-step problems.
Context Window
The context window is the maximum amount of text (in tokens) a model can consider at once — prompt plus its answer.
Embeddings
Embeddings turn text into numeric vectors so similar meanings sit close together, powering semantic search and RAG.
Few-Shot Prompting
Few-shot prompting gives the model a few worked examples of the task so it copies the pattern for your real input.
Fine-Tuning
Fine-tuning further trains a model on your examples to permanently shift its style or behavior — heavier than prompting or RAG.
Negative Prompting (Constraints)
Negative prompting tells the model what NOT to do — no jargon, no preamble, don't invent figures — to keep output on the rails.
One-Shot Prompting
One-shot prompting includes exactly one worked example to anchor the model's format and style.
Output Formatting
Output formatting is instructing the model to return a specific structure — a table, JSON, bullets — so results are usable and consistent.
Prompt Chaining
Prompt chaining breaks a big task into a sequence of prompts, feeding each step's output into the next.
Prompt Engineering
Prompt engineering is the practice of structuring instructions so a language model reliably produces the output you want.
Prompt Injection
Prompt injection is an attack where hidden or malicious text overrides an AI's instructions to make it misbehave or leak data.
Retrieval-Augmented Generation (RAG)
RAG feeds the model relevant documents at query time so its answer is grounded in your source data, not just its training.
Role Prompting
Role prompting tells the model to adopt a specific persona or expertise ('act as a tax attorney') to shape its answer.
Self-Consistency
Self-consistency runs a reasoning prompt several times and takes the most common answer, improving reliability on hard problems.
Self-Critique (Reflection)
Self-critique asks the model to review and improve its own answer, catching errors and weak spots before you use it.
System Prompt
A system prompt is a persistent instruction that sets the model's role, rules, and behavior for the whole conversation.
Temperature (LLM)
Temperature is a setting that controls randomness in AI output: low is focused and consistent, high is varied and creative.
Token
A token is the chunk of text an AI model reads and generates — roughly a word or word-piece; usage and limits are measured in tokens.
Top-p (Nucleus Sampling)
Top-p limits the model to the smallest set of words whose combined probability reaches p, controlling output diversity.
Zero-Shot Prompting
Zero-shot prompting asks the model to do a task with only an instruction and no examples.
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