Option B is correct because while schema verification itself doesn't incur extra cost, a large or complex schema can increase token consumption. Providing precise and concise descriptions for schema fields helps the LLM understand and adhere to the desired format more efficiently, potentially reducing the overall tokens consumed for accurate responses. Option C is correct as the "COUNT _ TOKENS' function allows developers to determine the token count of an input prompt for a specific model, enabling them to pre-emptively avoid exceeding the model's context window, thus preventing errors and wasted compute from re-runs. Option E is correct because for multi-turn conversations in Cortex Analyst, a summarization agent is specifically used to rephrase follow-up questions by incorporating previous context, without passing the entire, potentially long, conversation history. This significantly reduces the 'prompt_tokens' sent to the main LLM for each turn and optimizes inference times. Option A is incorrect because 'COMPLETE' (and 'TRY_COMPLETE) functions are stateless; to maintain conversational context, all previous user prompts and model responses must be included in the array, which increases token count proportionally. Simply sending the latest prompt would lose context. Option D is incorrect as setting a higher 'temperature' value (e.g., 0.7) increases the 'randomness and diversity' of the LLM's output, not necessarily its conciseness for cost optimization. For the most consistent (and often direct) results, a 'temperature' of 0 is recommended.