AI Glossary
Plain-language explanations of AI terms and concepts.
8 terms defined
AI Agent
وكيل ذكاء اصطناعي
An AI system that decides on its own which tool to call, checks what it gets back, and repeats that loop — planning and acting across multiple steps — rather than just replying once to a single message.
Learn more →Context Window
نافذة السياق
The maximum amount of text — measured in tokens, not words — that a model can hold in view at once, including everything you've said in the conversation so far, any documents you've pasted in, and the reply it's generating.
Learn more →Fine-tuning
الضبط الدقيق
Additional training applied to an already-built model, using your own examples, so it consistently adapts to a specific task, tone, or format — different from prompting, which shapes a single response rather than the model's underlying behavior.
Learn more →Hallucination
الهلوسة
When a model states something confidently that isn't true — not because it's lying, but because it always generates the most statistically plausible next words, and plausible isn't always the same as correct.
Learn more →Large Language Model (LLM)
نموذج لغوي كبير
An AI model trained on an enormous amount of text, built to predict the most likely next word given everything written so far — that single mechanism, repeated, is what produces writing, code, and conversation that reads as fluent and often accurate.
Learn more →Prompt
برومبت
The instruction you give a model — in plain language — to tell it what you want produced. It can be a single sentence or several paragraphs, and its wording shapes the output far more than most people expect.
Learn more →RAG (Retrieval-Augmented Generation)
التوليد المعزز بالاسترجاع
A technique where the model first retrieves relevant documents from a specific source — your files, a database, a website — and then generates its answer grounded in what it actually found, rather than relying only on what it memorized during training.
Learn more →Token
توكن
The small chunks of text a model actually reads and writes — not quite words, not quite characters. A token is roughly four characters of English on average, and every model's pricing, context window, and output limits are measured in tokens, not words.
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