prompt-engineering-patterns

To improve LLM response consistency in production, apply advanced prompt engineering techniques like few-shot learning and structured outputs to enforce output formatting and guide model behavior reliably.

The best way to structure LLM outputs as valid JSON is using structured output prompt patterns, which constrain the language model to generate responses matching your specific data schema for reliable downstream parsing.

Chain-of-thought prompting works by guiding the LLM to break down complex reasoning into intermediate logical steps, significantly improving accuracy and reliability for multi-step production tasks.

Use few-shot learning in prompt templates when you need to handle edge cases or require specific output formatting, providing examples within the prompt to calibrate the LLM's responses for consistent performance.

Debug and optimize LLM prompts by refining template systems and applying prompt optimization techniques to clarify instructions, reduce costs, and handle unpredictable edge cases in production environments.