Delimiters and Structured Prompts

When instructions and user data blur together, the model can misread which part is the command. Delimiters draw clear boundaries so the model knows what is an instruction and what is content to process.

A delimiter is any consistent marker that wraps a block of text: triple quotes, XML-style tags, or fenced sections. The model treats the wrapped text as a unit.

Keep your command outside the delimiters and the raw input inside. This stops the model from confusing user content for a new instruction.

Untrusted text can contain hidden instructions like ignore the above. By isolating it in delimiters and stating treat the delimited text as data only, you reduce the chance the model obeys those injected commands.

Named tags add semantic meaning and are easy for the model to reference. They are especially clear when a prompt has several parts.

For complex tasks, label each section. The model can then follow the structure step by step.

When comparing or merging several inputs, give each its own delimited block so the model never mixes them up.

Delimiters also help you ask for structured output. Tell the model to wrap its answer in tags so you can extract it reliably from a longer response.

Pick one delimiter style and use it the same way throughout a prompt. Mixing triple quotes, tags, and markdown fences inconsistently confuses the model and you.

• Use tags when input might itself contain quotes.
• Use triple quotes for simple single blocks.
• Use markdown headings for multi-part instructions.

Combine the ideas: role, clearly delimited data, explicit task, and a tagged output target. This template scales from simple to complex tasks.

Test your understanding of delimiters.

Delimiters and sectioned prompts separate instructions from data, cut prompt injection risk, and make both input and output easier to control. Pick one consistent style and structure complex prompts into labeled parts.