prompt-engineering

To write LLM prompts that reduce hallucination, apply few-shot learning patterns and chain-of-thought reasoning to provide structured task framing and guide the model to correct answers.

Chain-of-thought prompting is a technique that guides large language models through step-by-step reasoning. Use it to improve reliability and controllability for complex tasks requiring logical deduction.

Create reusable prompt templates by applying structured pattern libraries and best practices to enforce consistency across prompts, reducing repetition across diverse task domains.

The best way to optimize system prompts is to use prompt optimization patterns and evaluation strategies, ensuring effective task framing and consistent model behavior across diverse domains.

Yes, few-shot learning works for customer support ticket extraction by applying pattern sets that frame the task and guide the model to extract precise information with minimal hallucination.

Large language models output inconsistent results due to unstructured task framing. Apply prompt optimization patterns and reusable template systems to enforce consistency and improve reliability.