prompt-engineering-patterns

Production-grade LLM prompts apply structured patterns like few-shot selection and chain-of-thought to improve consistency, reliability, and controllability. Choose a pattern matching your task, apply it to the prompt, and test with varied inputs to refine the output.

The few-shot pattern dynamically selects examples to include in an LLM prompt, improving generalization across tasks. It provides the model with relevant context, helping it understand the expected output format and reasoning process for complex queries.

Chain-of-thought prompting improves LLM reasoning by guiding the model through intermediate logical steps before reaching a conclusion. Combined with self-consistency techniques, it verifies results and significantly enhances reliability for complex reasoning tasks.

You can enforce structured outputs from LLMs using template systems and specific prompt patterns. These enforce parseable responses, allowing safer integrations and reuse across projects by ensuring the model adheres to a defined output schema.

Design system prompts using integration patterns to align LLMs with business goals and constraints. This involves defining clear instructions, boundaries, and roles within the prompt to ensure controllable and consistent model behavior across domains.

Numpy is required as a dependency to run the scripts provided within this prompt engineering pattern framework. It supports the internal processing logic for template generation and few-shot example selection within the Python environment.