A Generative Al Engineer is using an LLM to classify species of edible mushrooms based...

The LLM classifies mushroom species accurately but includes unwanted reasoning text, and the engineer wants only the label. Let’s assess how to control output format effectively.

Option A: Use few shot prompting to instruct the model on expected output format

Few-shot prompting provides examples (e.g., input: description, output: label). It can work but requires crafting multiple examples, which is effort-intensive and less direct than a clear instruction.

Databricks Reference : " Few-shot prompting guides LLMs via examples, effective for format control but requires careful design " ( " Generative AI Cookbook " ).

Option B: Use zero shot prompting to instruct the model on expected output format

Zero-shot prompting relies on a single instruction (e.g., “Return only the label”) without examples. It’s simpler than few-shot but may not consistently enforce succinctness if the LLM’s default behavior is verbose.

Databricks Reference : " Zero-shot prompting can specify output but may lack precision without examples " ( " Building LLM Applications with Databricks " ).

Option C: Use zero shot chain-of-thought prompting to prevent a verbose output format

Chain-of-Thought (CoT) encourages step-by-step reasoning, which increases verbosity—opposite to the desired outcome. This contradicts the goal of label-only output.

Databricks Reference : " CoT prompting enhances reasoning but often results in detailed responses " ( " Databricks Generative AI Engineer Guide " ).

Option D: Use a system prompt to instruct the model to be succinct in its answer

A system prompt (e.g., “Respond with only the species label, no additional text”) sets a global instruction for the LLM’s behavior. It’s direct, reusable, and effective for controlling output style across queries.

Databricks Reference : " System prompts define LLM behavior consistently, ideal for enforcing concise outputs " ( " Generative AI Cookbook, " 2023).

Conclusion : Option D is the most effective and straightforward action, using a system prompt to enforce succinct, label-only responses, aligning with Databricks’ best practices for output control.