What common issue arises when using LLMs to generate structured data?	The model often includes headers, footers, or explanatory commentary alongside the requested data.
What technique can be used to ensure an LLM outputs only raw, structured data?	Combining stop sequences with pre-filling an assistant message.
In the context of structured data generation, what does pre-filling an assistant message instruct the model to do?	It instructs the model to assume it has already started the desired format (e.g., starting a JSON block).
What is the function of a stop sequence in controlling LLM output?	It immediately halts generation when the model encounters that specific sequence of characters.
How do pre-filling an assistant message and setting a stop sequence work together to isolate raw data?	The pre-fill starts the format, and the stop sequence cuts off the model before it can add concluding commentary or closing tags.
Is the technique of using stop sequences and pre-filling limited only to generating JSON?	No, it can be used for any kind of structured data, such as Python code or bulleted lists.
If you pre-fill the assistant message with a markdown code block start (e.g., ` ```json`), what should the stop sequence be?	The matching closing markdown code block (e.g., ` ``` `).
After using stop sequences to isolate raw data, what common post-processing steps might be necessary?	Parsing the response as JSON or using a strip function to remove extra newline characters.