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Temperature is a decoding parameter that controls randomness in model output selection. Lower temperature is more deterministic, while higher temperature produces more varied responses.
Tuning temperature helps teams balance consistency and creativity depending on the task.
A customer support assistant runs at low temperature for predictable answers, while a brainstorming assistant uses higher temperature for more diverse ideas.
Inference
Inference is the process of using a trained AI model to make predictions or generate outputs on new, unseen data. While training is about learning patterns, inference is about applying what the model has learned to real-world inputs.
Prompt Engineering
Prompt engineering is the practice of crafting and refining the instructions (prompts) given to an AI model to get the best possible output. It involves techniques like providing context, examples, and constraints to guide the model's response.
Large Language Model (LLM)
A large language model is an AI system trained on vast quantities of text data that can understand, generate, and reason about human language. LLMs use the transformer architecture and contain billions of parameters, enabling them to perform a wide range of language tasks.
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