Techniques & Methods
Completion
In LLM APIs, a "completion" is the model's generated response to an input prompt. The term originates from early OpenAI API design where the fundamental task was "text completion"—continuing a given text. Modern chat APIs use messages and responses but the underlying mechanism is identical.
Completion quality depends on prompt design, model capability, decoding parameters, and context length. Monitoring completion quality in production (via sampling, human review, or automated scoring) is essential for maintaining AI application reliability.
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Related Terms
Techniques & Methods
Prompt
Text input provided to an AI model to guide the content and format of its response.
Techniques & Methods
Generation
Producing new text, code, or content based on learned patterns and a given input prompt.
Techniques & Methods
Sequence Generation
Process where models produce sequences—such as words or tokens—based on learned patterns.
Techniques & Methods
Inference
Using a trained AI model to generate predictions or responses on new, unseen data.