prompt-engineering

To write reliable LLM prompts for automated agent workflows, apply techniques like few-shot learning, chain-of-thought prompting, and structured template systems to ensure consistency and safety across AI interactions.

The best way to create reusable prompt templates is defining a concrete command structure with system prompts and integration patterns, guiding the AI to generate structured outputs like meeting summaries with specific sections.

Use few-shot learning to provide specific examples that guide output formatting, while chain-of-thought prompting helps break down complex reasoning steps for AI-assisted decision making and production prompt optimization.

Designing prompts for sub-agents requires crafting specific commands and hooks within your template system, ensuring each sub-agent receives clear instructions to maintain performance and reliability.

Yes, system prompts improve safety in production LLM applications by establishing baseline rules and quality assurance patterns, which guide the model's behavior and prevent inconsistent or unsafe outputs.