AI model
A system trained on data to produce an output from an input. One model can be offered through several applications.
#A practical artificial intelligence glossary
Short, precise definitions to understand product claims, ask better questions and evaluate tools. 28 terms organized by use.
28 terms
Foundations
Large language model (LLM)
A model trained to process and generate language. It can write or summarize, but it is not a guaranteed database of facts.
#Token
A unit of text processed by a model: a word, part of a word or punctuation. Limits and costs are often expressed in tokens.
#Context window
The maximum amount of information a model can consider in one request, including input and output.
#Interacting with a model
Prompt
Instructions and context given to an AI system. A useful prompt states the intended result, constraints and format.
#System instruction
An instruction shaping an assistant’s behavior. It does not replace technical access and data controls.
#Multimodal
The ability to process or produce several content types, such as text, images, audio and video.
#Temperature
A parameter affecting output diversity in some models. A lower value does not guarantee accuracy.
#Answers and sources
Hallucination
A plausible but false, invented or unsupported answer. A source or citation can also be fabricated.
#Grounding
A method linking an answer to identifiable documents. It helps verification, but a source can still be misread.
#Retrieval-augmented generation (RAG)
An architecture retrieving passages from a corpus before passing them to a model for its answer. Quality also depends on the documents and retrieval.
#Source citation
A reference shown with an answer so a claim can be traced. Open the source and check the passage, date and context.
#Architecture
Embedding
A numerical representation used to compare semantic similarity. Nearby items are not necessarily equivalent or true.
#Vector database
A system storing vectors and retrieving nearby items. In RAG it helps select passages but does not verify their accuracy.
#Fine-tuning
Additional training on targeted examples to change model behavior. It does not replace an up-to-date document collection.
#Application programming interface (API)
A technical interface through which an application requests an AI service. Costs, quotas and data rules depend on the plan.
#Agents and automation
AI agent
A system combining a model, goals, tools and decision steps to complete a task. Its autonomy should be bounded by permissions.
#Tool calling
A mechanism through which a model asks an application to run a function such as search or calculation. The application controls execution.
#Workflow
A sequence of steps connecting people, data and tools. Reliable automation plans for errors, recovery and approval.
#Human review
A decision or check performed by a person before or after a system action, useful when errors could have significant consequences.
#Evaluation
Evaluation set
A stable set of representative cases and criteria used to compare versions of an AI system on a defined task.
#Benchmark
A standardized evaluation comparing systems under a stated method. A strong public score does not guarantee quality on your data.
#Regression
A decline in behavior that previously worked, sometimes after a change to a model, prompt or data.
#Latency
The time between a request and a usable result. Also measure retrieval, tool execution and human review time.
#Data and rights
Personal data
Information that identifies a person directly or indirectly. Check necessity and processing terms before sharing it.
#Data retention
The period for which a service stores inputs, outputs or logs. It varies by product, plan and settings.
#Open weights
A model whose trained parameters are distributed under a license. It does not always mean the data or code is open.
#Usage license
Terms governing use, redistribution or modification of a model, tool or content. Check the exact license before commercial use.
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