A developer is building an interactive chat application in Snowflake leveraging the COMPLETE (SNOWFLAKE. CORTEX) LLM function to power multi-turn conversations. To ensure the LLM maintains conversational context and generates coherent responses based on prior interactions, which of the following methods correctly implements the passing of conversation history to the COMPLETE function?

Correct Answer: B
To provide a stateful, conversational experience with the 'COMPLETE (SNOWFLAKE.CORTEX)' function, all previous user prompts and model responses in the conversation must be passed as part of the argument. This argument is an array of objects, with each object representing a turn in the conversation and containing a 'role' ('system', 'user', or 'assistant') and a 'content' key, presented in chronological order. Option A is less effective as it loses the structured conversational context that roles provide. Option C is incorrect because 'COMPLETE' does not retain any state from one call to the next; conversational history must be explicitly managed and passed. Option D describes a non-existent parameter for the 'COMPLETE function. Option E, while fine-tuning is a Snowflake Cortex capability, it is used to customize a model for a specific task over time, not for real-time maintenance of dynamic conversational context in a multi-turn chat.