prompt-engineering-patterns

To ensure consistent JSON from an LLM, apply structured output patterns and error handling techniques. This approach enhances parsing reliability and controllability for downstream processing in production applications.

Few-shot learning is an advanced prompt engineering technique that provides examples within the prompt to guide LLM behavior. Use it to improve output quality and consistency in production applications.

Optimize LLM prompts to reduce token usage by applying production optimization techniques like refining template systems and system prompt design. This improves output quality and parsing reliability while minimizing token consumption.

Chain-of-thought prompting improves LLM reliability by structuring the model's reasoning process step-by-step. This advanced pattern enhances performance and controllability for complex production tasks.

The best prompt engineering patterns for robust LLM integration include chain-of-thought, few-shot learning, and structured outputs. These techniques maximize performance, reliability, and controllability in production environments.

LLM output fluctuates due to unstructured prompts; template systems fix this by standardizing system prompt design and applying consistent patterns. This ensures reliable output generation and robust integration in production applications.