Nov 6 (edited) • General Discussion
By Request - 20 AI & Large Language Model Terms Made Simple
I put together a quick glossary of the 20 most common AI and Large Language Model terms, no tech degree required.
Whether you’re just starting out or already building with AI, this list will help you understand the language everyone’s using.
👉 Save this for reference and share it with anyone starting their AI journey.
AI can sound like another language, so here’s a quick glossary you can bookmark and come back to when you need a refresher. Whether you’re just exploring or already experimenting, these terms will help you speak “AI” with confidence.
AI (Artificial Intelligence): Technology that mimics human thinking and learning to perform tasks like writing, analyzing data, or predicting outcomes.
Machine Learning (ML): A type of AI that improves automatically through experience, like teaching a computer to recognize patterns.
Deep Learning: A branch of machine learning that uses layered neural networks (similar to how the human brain works) to handle complex tasks such as image or speech recognition.
Neural Network: A system of algorithms designed to recognize relationships in data, inspired by how our brains process information.
Large Language Model (LLM): A type of AI trained on huge amounts of text so it can understand and generate natural language (like ChatGPT).
Training Data: The text, images, or other information used to teach an AI how to recognize patterns or make predictions.
Parameters: The internal settings an AI adjusts during training to learn how to produce better results.
Prompt: The instruction or question you give an AI to get a response.
Prompt Engineering: The skill of crafting better prompts to get more accurate or creative outputs.
Token: A small piece of text, like a word or part of a word, that AI models use to process language.
Context Window: The limit to how much text an AI model can “remember” or consider at once in a conversation.
Fine-Tuning: Customizing an existing AI model by training it further on specific data so it performs better for a particular task.
Embedding: A way to turn text into numbers so the AI can measure meaning and similarity between words or phrases.
Inference: The process of the model generating a response or prediction based on what it has learned.
Retrieval-Augmented Generation (RAG): Combining an AI model with a database or documents so it can look up information before responding.
Agent: An AI that can take multiple actions or chain steps together to complete a task, such as researching, summarizing, and writing.
API (Application Programming Interface): The bridge that lets apps connect to AI models like GPT to perform functions automatically.
Generative AI: AI that creates new content such as text, images, music, or code instead of just analyzing existing data.
Bias: When AI reflects the imbalances or prejudices found in its training data, which can lead to unfair or skewed results.
Hallucination: When an AI confidently makes up something that isn’t true, reminding us to always fact-check its outputs.
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By Request - 20 AI & Large Language Model Terms Made Simple
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