A Platform Administrator created a new prompt template and is testing it. Every time the administrator tests the template, it gives a different response. Why is the prompt template giving different responses each time it’s run?
A.
Prompt Builder caches the large language model’s response, so the prompt is only sent once for every template the administrator creates.
B.
Every time the administrator runs a prompt template in Prompt Builder, it creates a unique call to the large language model.
C.
The prompt is only sent to the large language model after the administrator deploys the template to a live agent, not when the prompt is run in the builder.
D.
Prompt Builder runs a simulated call to the large language model to save on costs, so the prompt is never sent to the actual model.
Generative AI models are inherently probabilistic, meaning they do not produce the exact same output every time, even when given the same input. When a Platform Administrator uses the Prompt Builder to test a template, each "Preview" or "Test" execution initiates a fresh, unique call to the Large Language Model (LLM). Because the LLM considers a range of linguistic possibilities and probabilities for each word it generates, the resulting text will vary slightly (or significantly) with each iteration. This behavior is expected and is a core characteristic of generative AI. To ensure consistent and high-quality results, administrators must refine their instructions to be as specific as possible, narrowing the LLM's range of creative interpretation. Options A, C, and D are incorrect because the Prompt Builder is designed to provide real-time feedback from the actual LLM to help administrators debug and perfect the prompt's logic before it is deployed to end-users or agents.
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