prompt-engineering

To write a system prompt for an LLM, define specific roles, constraints, and examples for your tasks. Using structured formats like Markdown and XML tags helps separate context, instructions, and examples to precisely control model behavior.

The best way to structure LLM prompts is using Markdown combined with XML tags to partition elements like context, tasks, and examples. This structural separation ensures clear instruction-tuning across different languages and domains.

Yes, you can use XML tags inside Markdown to separate context and examples in system prompts. This technique isolates specific prompt elements, ensuring the LLM accurately processes distinct instructions and constraints without signal confusion.

Creating effective instructions and roles for LLM instruction-tuning requires defining clear constraints and providing examples within a structured system prompt. This approach guides the model's responses across varied scenarios and domains.

Crafting system prompts works across different languages and domains by adjusting the defined roles, constraints, and examples. The underlying structural prompt design remains effective regardless of the specific linguistic or domain context.