Artificial intelligence
Artificial intelligence covers methods such as language processing and pattern recognition. Suitability is assessed against a specific task.
Address a task using AI methods
Artificial intelligence, or AI, is an umbrella term for methods that computer systems use for tasks such as language processing, pattern recognition and content generation. Machine learning is a subfield. The label alone reveals little about which task a system performs reliably.
In a fictional example, a model assigns incoming customer emails to delivery or invoice categories. Suitable examples establish whether that assignment is useful. A fluent explanation does not replace the correct category.
Automation initially describes a process running automatically. A scheduled file transfer can be automated without an AI model. When choosing a solution, its actual input, intended outcome and handling of failures therefore tell you more than the AI label.
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Related terms
- Large language modelA large language model processes language using learned patterns. Fluent text proves neither factual accuracy nor access to current business data.
- Generative AIGenerative AI produces content such as text, images or code. Quality is assessed against its intended use and available source information.
- Text classificationText classification assigns text to predefined categories. Categories and the handling of ambiguous messages must fit the task.
- Model cardA model card documents an AI model’s purpose, foundations, evaluation conditions and known limitations for a particular version.
- AI readinessAI readiness concerns the foundations of an AI project: objectives, usable data, skills, responsibilities and an operating workflow.
- Context engineeringContext engineering prepares information for individual model calls: relevant instructions, messages, sources and current states.
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