prompt-engineering-patterns

To get reliable structured JSON output from an LLM, use Pydantic schemas to define the expected response format. This ensures the generated output strictly adheres to your required data structure for production environments.

The best way to implement chain-of-thought prompting is by using patterns that elicit step-by-step reasoning from the LLM. Tree-of-thought techniques further enhance this by exploring multiple reasoning paths for complex problem-solving.

Improve few-shot learning by dynamically selecting the most relevant examples for a specific query. This technique aligns the prompt's context with the input, significantly enhancing the LLM's accuracy and contextual understanding.

Design system prompts for LLM optimization by utilizing template systems and established prompt engineering patterns. This approach maximizes controllability, reliability, and overall LLM performance in production environments.

LLM prompts return inconsistent results due to suboptimal design and lack of controllability. Fix this by applying advanced prompt optimization patterns, structured output generation using Pydantic schemas, and dynamic few-shot example selection.