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prompt-engineering-patterns

Design and optimize LLM prompts with few-shot learning and template systems.

6|Updated Mar 24, 2023
Search Tags:#prompt engineering#prompt optimization#few-shot learning#LLM#template systems
One-click install
npx skills add https://github.com/GaoZimeng0425/nemo-cli --skill prompt-engineering-patterns-gaozimeng0425
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/GaoZimeng0425/nemo-cli/tree/main/.claude/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/GaoZimeng0425/nemo-cli --skill prompt-engineering-patterns-gaozimeng0425

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill empowers you to optimize prompts for LLMs, enhancing performance, reliability, and controllability.

Core Features & Use Cases

  • Advanced Prompt Engineering: Techniques for maximizing LLM output quality and consistency.
  • Few-Shot Learning: Implement dynamic example selection and chain-of-thought reasoning.
  • Prompt Optimization: Refine prompts iteratively for improved performance.
  • Template Systems: Build reusable prompt templates with variable interpolation.
  • System Prompt Design: Establish clear model behavior and output formats.

Quick Start

To generate a structured prompt for a text classification task, use the following Python code:

from prompt_optimizer import PromptTemplate, FewShotSelector
# Define a structured prompt template
template = PromptTemplate(
    system="You are an expert SQL developer. Generate efficient, secure SQL queries.",
    instruction="Convert the following natural language query to SQL:",
    few_shot_examples=True,
    output_format="SQL code block with explanatory comments"
)
# Configure few-shot learning
selector = FewShotSelector(
    examples_db="sql_examples.jsonl",
    selection_strategy="semantic_similarity",
    max_examples=3
)
# Generate optimized prompt
prompt = template.render(
    query="Find all users who registered in the last 30 days",
    examples=selector.select(query="user registration date filter")
)

Frequently Asked Questions about prompt-engineering-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement few-shot learning for LLM prompt optimization?▼

Few-shot learning for LLM prompt optimization is implemented through dynamic example selection and chain-of-thought reasoning, using semantic similarity strategies to match query contexts with relevant examples and improve output consistency.

What is the best way to build reusable prompt template systems?▼

Building reusable prompt template systems involves creating structured templates with variable interpolation, establishing clear system prompts, and defining specific output formats to maximize LLM performance and controllability across different tasks.

How do I design system prompts to control LLM behavior and output formats?▼

Designing system prompts to control LLM behavior requires establishing clear model behavior guidelines and explicit output formats, such as SQL code blocks with explanatory comments, to ensure reliable and structured responses.

Does prompt engineering with chain-of-thought prompting require prior ML knowledge?▼

Prompt engineering with chain-of-thought prompting requires an understanding of AI and ML concepts, particularly around language models and prompt design, to effectively implement dynamic example selection and iterative refinement.

Why does my LLM output fluctuate and how can prompt optimization fix it?▼

LLM output fluctuates due to inconsistent instructions or poorly structured inputs; prompt optimization fixes this by iteratively refining prompts, applying few-shot learning, and standardizing template systems to enhance reliability.

What are the limitations of few-shot learning for complex text classification tasks?▼

Limitations of few-shot learning for complex text classification include dependency on the quality of the examples database and the semantic similarity selection strategy, which may not capture highly nuanced contexts without iterative prompt optimization.

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