prompt-engineering-patterns

Prompt engineering improves LLM consistency by using reusable templates and system prompts to enforce strict formatting and behavioral rules. This ensures your application generates reliable, controllable outputs.

Chain-of-thought prompting is a technique that guides an LLM through intermediate reasoning steps before generating a final answer. Use it to enhance performance and reliability in complex logic or multi-step application scenarios.

To design a few-shot prompt for sentiment analysis, provide the LLM with several input text examples paired with their correct sentiment labels. This few-shot learning technique calibrates the model to classify text accurately.

Yes, you can use prompt optimization techniques for customer service chatbots. By engineering system prompts and implementing error recovery, you ensure chatbot responses remain accurate, consistent, and correctly formatted.

The best way to structure system prompts for production AI applications is by designing reusable template systems. This approach allows you to define robust instructions and implement error recovery for reliable performance.