prompt-engineering-patterns

What problem does it solve?

Maximizing the performance, reliability, and controllability of Large Language Models (LLMs) in production requires sophisticated prompt engineering. This Skill provides advanced techniques to design, optimize, and manage prompts for consistent and high-quality outputs.

Core Features & Use Cases

• Few-Shot Learning: Guides on example selection strategies (semantic similarity, diversity) and context window management.
• Chain-of-Thought Prompting: Covers zero-shot, few-shot, self-consistency, and advanced reasoning patterns like Tree-of-Thought.
• Prompt Optimization: Explains iterative refinement, A/B testing, token reduction, and latency optimization.
• Use Case: When developing a critical LLM application, this Skill helps you design prompts that reduce hallucinations, improve accuracy, and ensure the model follows specific output formats, leading to more reliable AI systems.

Quick Start

Example: Structured prompt template with few-shot learning

This demonstrates how to define a template and select examples dynamically.

from prompt_optimizer import PromptTemplate, FewShotSelector

template = PromptTemplate( system="You are an expert SQL developer.", instruction="Convert natural language to SQL: {query}", few_shot_examples=True, output_format="SQL code block" )

selector = FewShotSelector(examples_db="sql_examples.jsonl")

prompt = template.render(query="Find users", examples=selector.select(...))