AI0-001 Implementing AI Solutions Practice Question
An AI team is deploying a fine-tuned LLM for a code generation assistant. They need to ensure the model outputs only syntactically valid JSON for integration with downstream systems. Which prompt engineering technique is MOST effective for enforcing structured output?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable JSON mode in the API call, specifying the desired JSON schema
JSON mode (or structured output) instructs the model to output valid JSON, often with schema enforcement. Few-shot examples may help but aren't as reliable as a dedicated mode. System prompts set the role but don't enforce syntax. Chain-of-thought improves reasoning but not format.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable JSON mode in the API call, specifying the desired JSON schema
Why this is correct
JSON mode forces the model to output valid JSON, often with schema constraints, ensuring downstream parsability.
- ✗
Provide a few-shot example of a valid JSON response in the prompt
Why it's wrong here
Few-shot examples guide the model but may not be sufficient for complex constraints; the model can still deviate.
- ✗
Include a system prompt that says 'You are a helpful coding assistant.'
Why it's wrong here
A system prompt sets the assistant's role but does not enforce a specific output format.
- ✗
Use chain-of-thought prompting to have the model reason step-by-step before answering
Why it's wrong here
Chain-of-thought improves reasoning quality but does not guarantee structured JSON output.
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