Option A is incorrect. The user's query is typically embedded (e.g., using ' to perform a similarity search against the Cortex Search Service. The 'retrieved documents' (context) are then passed to the function, not the embedding function itself. Option B is correct because to provide a stateful, conversational experience, all previous user prompts and model responses should be passed in the array to the 'COMPLETE' or function. Option C is incorrect. While concatenation is a method, for better accuracy and control, the retrieved context should be integrated into a well-engineered prompt, often using tags or specific instructions, rather than just raw concatenation, to guide the LLM's response. Option D is correct because 'AI_COMPLETE Structured OutputS allows you to supply a JSON schema that completion responses must follow, reducing the need for post-processing and enabling seamless integration with systems requiring deterministic responses. Option E is incorrect. While keeping processing within Snowflake is good for data govemance, complex RAG pipelines often involve multiple distinct steps (query embedding, search, retrieval, LLM completion) that may benefit from a staged approach rather than a single monolithic SQL statement. The optimal approach depends on the specific complexity and performance requirements, and a single 'SELECT for the 'entire' RAG flow might not always be the most efficient or practical solution.