prompt-engineering

To structure prompts for iterative reasoning, use the ReAct pattern to combine reasoning traces with tool use actions, enabling agents to process complex tasks step-by-step. It ensures structured interaction and optimal AI performance.

Chain-of-Thought prompting follows a single linear reasoning path, while Tree-of-Thoughts explores multiple reasoning branches simultaneously. Tree-of-Thoughts is suited for complex tasks requiring exploration of different decision paths.

Inject agent memory context efficiently within token limits by applying structured prompt engineering patterns that prioritize relevant information. This ensures memory is utilized optimally without exceeding the model's token constraints.

Use Constitutional AI patterns when your prompts require strict adherence to safety principles and specific behavioral guidelines. This pattern ensures AI outputs remain aligned with predefined rules during complex tasks.

A Fleet Reasoning Pipeline executes complex tasks by chaining multiple structured prompt patterns like ReAct and Few-Shot across an agent workflow. It enables structured reasoning and context injection across multiple processing stages.

Few-Shot prompting works with agent-memory interactions by providing example-based context injection to guide the AI's response format. It leverages stored memory examples to ensure clarity and efficiency in complex tasks.