prompt-engineering-patterns

To optimize LLM prompts for production, apply iterative refinement, A/B testing, and token management to control latency and ensure consistent, reliable model behavior in your application.

Implement few-shot learning by embedding targeted examples within your system prompts to guide the LLM's behavior, ensuring accurate and contextually relevant outputs for specific tasks like sentiment analysis.

Chain-of-thought prompting improves LLM controllability by structuring the prompt to guide the model through intermediate reasoning steps, resulting in more accurate and transparent outputs for complex queries.

LLM prompts become inconsistent in production without iterative refinement, A/B testing, and strict token management to manage latency and enforce reliable, controlled model behavior.

You can reduce token usage and latency by applying prompt optimization workflows and template systems that manage token efficiency while maintaining the LLM's performance and output reliability.

Prompt template systems are essential for effective A/B testing, allowing you to systematically manage, iterate, and refine prompt variations to measure performance and reliability improvements.