Completions (LLM)
The output generated by a large language model in response to an input prompt. A completion is whatever the model produces — a sentence, a paragraph, a JSON object, a decision — based on the instructions and context it was given. The quality of a completion depends on the clarity of the prompt, the model's parameters, and how well the task fits the model's training.
What are LLM Completions?
In the context of large language models, a completion is the text (or structured data) a model generates in response to a prompt. You send an input — a question, an instruction, a document to analyze — and the model returns a completion: its best continuation of that input based on everything it learned during training. The term comes from the original framing of language models as text-completion engines, though modern models are used for far more than finishing sentences.
Every interaction with an LLM produces a completion. Whether you are asking a model to extract invoice fields, classify a support ticket, draft a supplier email, or reason through an exception — the model's response is a completion. Understanding how completions work helps you design prompts that produce the outputs you actually need.
What Determines Completion Quality
Three factors control what a completion looks like:
The prompt — the clearest lever. Vague instructions produce vague completions. Specific, well-structured prompts with examples produce reliable, consistent outputs.
Temperature — a parameter controlling how much randomness is in the output. Low temperature (close to 0) produces consistent, predictable completions. High temperature produces more varied outputs. For operational tasks, low temperature is almost always correct.
Model choice — larger, more capable models produce better completions on complex tasks but cost more per call and run slower. Matching model capability to task complexity is a real cost-optimization decision.
Completions in Operational Workflows
In automated back-office workflows, completions are the output of each AI processing step. An invoice arrives, a prompt asks the model to extract key fields and return them as JSON — the completion is that JSON object. Another prompt asks whether the extracted total matches the PO value and why — the completion is a structured reasoning output that feeds the next workflow step. Designing prompts for reliable, parseable completions is the core engineering challenge in building operational AI systems.