prompt-engineering

Prompt assembly constructs LLM agent prompts from multiple layers including identity, soul, tools, and configuration. The pipeline dynamically renders these layers using a template system to ensure consistent AI behavior.

To modify an LLM agent's soul text and identity configuration, consult the prompt engineering documentation for best practices on writing effective soul text and structuring identity layers within the agent platform.

The $placeholder syntax in agent prompt templates is used for dynamic prompt rendering. It allows variables to be resolved at runtime, enabling flexible prompt assembly across different prompt layers and channel-specific guidance.

Best practices for writing LLM tool guidance and prompt layers include following structured prompt writing principles, ensuring correct prompt configuration, and leveraging testing methodologies to validate agent behavior across different channels.

You need prompt engineering architecture when developing a new LLM agent or modifying an existing one. It ensures your prompts are structured correctly, adhere to best practices, and leverage the platform's capabilities efficiently.

You test prompt configurations and agent behavior by following the testing methodologies detailed in the prompt engineering architecture documentation. This ensures prompt layers, placeholder resolution, and channel-specific guidance function correctly.