Yes, to a large extent. Large language models (LLMs) are inherently stochastic: the same input can generate different outputs across multiple runs. Nevertheless, we keep the variability across multiple runs at a minimum by ensuring environment consistency, fixing the adjustable statistical parameters like sampling to sensible values and thoroughly testing the responses against a variety of benchmarks. Hence, repeating the same request can result in a slightly different word choice in the answer, but it is very unlikely that the overall intent changes.
Having said that some AI features are intentionally designed to incorporate variance:
Chat Responses: In chats, outputs may differ for the same query due to the influence of previous conversation context. Even when starting a new chat with the same question, slight variations ensure a natural conversational tone, enhance adaptability and support a broad range of applications.
Subcode Suggestions: This feature emulates a creative brainstorming process, offering different suggestions each time. The variability reflects the nuances within your coded data, promoting a broader range of ideas.
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