Temperature
Temperature is a sampling parameter that controls how random a language model's outputs are. It scales the logits, or raw scores, that the model assigns to each possible next token before a token is chosen. A lower temperature makes the model more conservative and deterministic; a higher temperature makes it more creative and varied. At temperature zero, the model almost always picks the highest-scoring token, which is ideal for tasks like code generation, factual answers, and structured output where consistency matters. At temperature one or above, the model is more willing to sample lower-scoring tokens, which can produce surprising phrasing, creative writing, and diverse brainstorming ideas. There is no universal best setting. Coding and data extraction usually benefit from low temperatures around 0.1 to 0.3. Marketing copy, fiction, and idea generation often feel better at 0.7 to 1.0. If outputs are too repetitive, raise the temperature. If they become erratic or off-topic, lower it.
Published 2026-06-12
Related terms
Explore the glossary
Find definitions for AI, LLM, MCP, RAG, agent, and prompt engineering terms.
Browse all termsRelated Resources
Prompt Engineering
GlossaryPrompt engineering is the practice of crafting inputs to a language model so it produces better outputs without changing the model's weights. It covers word choice, structure, examples, constraints, and the order in which information appears. A well-engineered prompt can turn a mediocre response into a precise, actionable one. Effective prompts are usually clear, specific, and formatted. They state the task, define the audience, set the output format, and include any constraints. Adding examples, known as few-shot prompting, helps the model understand patterns that are hard to describe in words. Breaking complex tasks into steps, called chain-of-thought prompting, improves reasoning and arithmetic. Prompt engineering is iterative. You write a prompt, test it on diverse inputs, measure the results, and refine. Tools like the VePrompts Prompt Optimizer can surface issues such as ambiguity, missing constraints, or conflicting instructions. Good prompt engineering is often the fastest way to improve an AI feature before investing in fine-tuning or custom infrastructure.
DeepSeek Coder Architect
PromptLeverage DeepSeek Coder for complex software architecture, code generation, and technical problem-solving with advanced reasoning.
3D Printing Optimizer
SkillOptimize 3D models for additive manufacturing considering orientation, supports, infill, and material properties.
Firecrawl
MCP ServerOfficial Firecrawl MCP Server - Adds powerful web scraping and search to Cursor, Claude and any other LLM clients.
Prompt
GlossaryThe input text given to a language model to elicit a desired response.