prompt-engineering

Design robust LLM prompts by applying established patterns like few-shot exemplars, chain-of-thought reasoning, and system prompts to enforce clear instructions, success criteria, and safety-aware design.

Structure reusable prompt templates by separating core instructions from variable inputs, incorporating pattern-based design, and applying consistent evaluation methods to maintain reliability across varied tasks.

Use chain-of-thought reasoning in LLM prompts when handling complex tasks that require intermediate logical steps, improving determinism and controllability by guiding the model through structured reasoning.

Few-shot exemplars improve LLM determinism by providing concrete examples of expected input-output mappings within the prompt, conditioning the model to replicate specific formatting and reasoning patterns.

Evaluate and refine system prompts by applying structured evaluation methods to test consistency and safety, iterating on instruction tuning to constrain model behavior and improve controllability.

Apply prompt-engineering patterns to instruction tuning by adding a system prompt, few-shot exemplars, and clear success criteria to user instructions, improving reliability and task performance.