prompt-engineer

To optimize LLM prompts for accuracy and token efficiency, engineer and refactor them using patterns like few-shot, Chain-of-Thought, and ReAct. This refines prompt design to improve reliability and reduce token consumption.

The best way to structure LLM outputs in JSON format is by applying structured output generation techniques like JSON mode and function calling. This ensures the language model reliably returns data in the required structured format.

You create an evaluation framework to test prompt performance by implementing systematic testing methods like automated testing, LLM-as-judge, and human review. This measures prompt reliability against defined metrics under diverse conditions.

You should use Chain-of-Thought prompting over zero-shot when tasks require complex reasoning and step-by-step logic. Zero-shot relies on direct instructions, whereas Chain-of-Thought breaks down problems to improve accuracy on difficult reasoning tasks.

Yes, you can use function calling to extract specific data from customer feedback. By designing prompts with structured output generation, you can reliably extract key sentiment and topics alongside summaries in a structured format.