prompt-engineering

To create system prompts for LLMs in production, use structured guidelines to clarify roles, enforce constraints, and define output formats. This ensures consistency and accuracy across automated tasks.

Defending against prompt injection involves applying specific constraint enforcement and safety strategies within your LLM prompts. This protects production workflows from malicious user inputs and unauthorized actions.

Temperature control affects LLM output formatting by调节 generation randomness. Lower temperatures ensure consistent, deterministic outputs, while specific settings guide structured formatting for various task types.

You can coordinate multiple AI agents by structuring prompts to ensure the right actions are taken in the correct order. This involves proper context management and role clarification for each agent.

You need evaluation sets to manage LLM prompt changes effectively. Combining them with semantic versioning allows you to track performance, maintain accuracy, and revert unsafe prompt modifications.