What Is Generative AI and How Does It Work?
Generative AI learns patterns from examples and uses those patterns to create new text, images, audio, code, or structured output in response to instructions.
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AI Fundamentals
Core ideas for understanding artificial intelligence.
AI Models vs. AI Applications: What Is the Difference?
A model provides underlying capability, while an application packages that capability with interface design, permissions, data controls, integrations, and support.
Read guideLarge Language Models Explained in Plain Language
Large language models predict useful sequences of language from patterns learned during training, then adapt their responses to the prompt and context they receive.
Read guideTokens, Context Windows, and Why They Matter
AI systems process language in tokens and can only consider a limited amount of context at one time, even when that limit is large.
Read guideMultimodal AI: Text, Images, Audio, and Beyond
Multimodal AI works across more than one type of information, such as reading an image, interpreting audio, and responding in text.
Read guideTraining, Fine-Tuning, and Retrieval: Three Ways AI Learns Context
Training builds general capability, fine-tuning adjusts behavior, and retrieval supplies current reference material when the model answers.
Read guideAI Agents Explained: From Answers to Actions
An AI agent combines model reasoning with tools and permissions so it can take steps such as searching, scheduling, drafting, or updating systems.
Read guideHow AI Hallucinations Happen and How to Reduce Them
AI hallucinations happen when a system produces plausible but unsupported information, often because the prompt, context, or model knowledge is insufficient.
Read guideHow to Evaluate the Quality of AI Output
Good AI output should be accurate, relevant, complete enough for the task, appropriately formatted, and clear about uncertainty.
Read guideChoosing an AI Model for a Specific Task
The right AI model depends on the task's complexity, required accuracy, speed, cost, privacy needs, tool access, and tolerance for error.
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