Prompt Engineering Technique Examples

via “chain-of-thought and advanced prompt engineering technique library”

Microsoft's unified LLM evaluation and prompt robustness benchmark.

Unique: Provides a modular library of prompt engineering techniques (CoT, Emotion Prompt, Expert Prompting) that can be applied, composed, and evaluated systematically. Each technique is implemented as a prompt transformation that can be combined with others and evaluated independently.

vs others: More systematic than ad-hoc prompt engineering because it provides reusable, composable techniques with built-in evaluation, whereas manual prompt engineering requires trial-and-error without structured comparison of techniques.

via “prompt engineering optimization toolkit”

Prompt optimization library with systematic variation testing.

Unique: Promptimize uniquely combines rigorous testing methodologies with automated improvement workflows for prompt engineering.

vs others: Unlike other prompt engineering tools, Promptimize offers a structured evaluation system that integrates A/B testing and performance tracking.

via “prompt enhancement for improved code generation quality”

A library of Agent Skills designed to work with the Stitch MCP server. Each skill follows the Agent Skills open standard, for compatibility with coding agents such as Antigravity, Gemini CLI, Claude Code, Cursor.

Unique: Implements prompt optimization as a discrete, reusable skill that preprocesses design specifications before code generation, treating prompt quality as a first-class concern. This approach separates prompt engineering from code generation, enabling independent optimization and reuse across multiple code generation tasks.

vs others: More systematic than ad-hoc prompt engineering because it's a structured skill with defined inputs/outputs, and more effective than single-stage code generation because it optimizes prompts before code generation, improving downstream model comprehension.

via “prompt-engineering-technique-aggregation”

A curated list of Generative AI tools, works, models, and references

Unique: Treats prompt engineering as a first-class capability with dedicated resources and subcategories, rather than burying it within LLM documentation. Recognizes that prompt design is a critical skill for LLM application development, separate from model selection or fine-tuning

vs others: More comprehensive than single-model documentation (OpenAI's prompt engineering guide) by covering techniques across multiple models, but less interactive than specialized platforms (Prompt.com, PromptBase) which provide prompt marketplaces and community sharing

via “prompt engineering with structured instruction design”

本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：https://datawhalechina.github.io/llm-universe/

Unique: Provides executable prompt engineering examples showing before/after comparisons of instruction quality, demonstrating how specific design choices (role definition, context framing, output format) improve response quality; includes Chinese language prompt examples for non-English applications

vs others: More practical than theoretical prompt engineering papers because it shows runnable examples; more comprehensive than single-technique tutorials because it covers multiple instruction patterns; more accessible than research papers because it uses beginner-friendly language and Jupyter notebooks

via “automatic prompt engineer (ape) technique for optimizing prompts through search”

🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

Unique: Presents APE as a meta-level prompting technique where LLMs are used to optimize prompts for other LLM tasks, showing how prompting techniques can be applied recursively to improve themselves

vs others: More scalable than manual prompt engineering for many tasks; more interpretable than black-box fine-tuning because optimized prompts remain human-readable; more automated than human-in-the-loop prompt engineering

via “prompt-engineering-techniques-with-model-specific-examples”

Comprehensive resources on Generative AI, including a detailed roadmap, projects, use cases, interview preparation, and coding preparation.

Unique: Includes executable Jupyter notebooks with Ollama-based models that demonstrate prompt engineering techniques in a reproducible, local-first environment, rather than requiring API calls to proprietary models. Enables experimentation without API costs or rate limits.

vs others: More practical than theoretical prompt engineering guides because it provides runnable examples with local models, allowing developers to experiment with techniques immediately without API dependencies or costs.

via “prompt-engineering-workflow-methodology-reference”

This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc

Unique: Provides structured workflow methodology for prompt engineering rather than isolated technique tips, documenting the iterative design-test-refine cycle with evaluation frameworks

vs others: More systematic than scattered blog posts because it provides end-to-end workflow; more practical than academic papers because it focuses on actionable methodology rather than theoretical foundations

via “prompt-engineering-technique-library-with-chain-of-thought”

PromptBench is a powerful tool designed to scrutinize and analyze the interaction of large language models with various prompts. It provides a convenient infrastructure to simulate **black-box** adversarial **prompt attacks** on the models and evaluate their performances.

Unique: Implements a modular library of prompt engineering techniques (CoT, Emotion, Expert, etc.) as composable transformations rather than hard-coded strategies, allowing researchers to apply, combine, and evaluate techniques systematically across datasets and models.

vs others: More comprehensive than single-technique tools because it provides multiple prompt engineering methods in one framework, enabling comparative evaluation and technique composition. Allows systematic study of which techniques work for which models/tasks.

via “prompt engineering and optimization interface”

Build powerful AI Agents for yourself, your team, or your enterprise. Powerful, easy to use, visual builder—no coding required, but extensible with code if you need it. Over 100 templates for all kinds of business and personal use cases.

via “agent customization and fine-tuning via prompt engineering”

Marketplace for autonomous AI workers with no-code

via “prompt engineering system with agent-specific templates”

Code the entire scalable app from scratch

Unique: Implements agent-specific prompt templates that are dynamically constructed with project context, previous decisions, and feedback history. Prompts are parameterized and versioned, enabling systematic improvement of agent behavior through prompt engineering.

vs others: Unlike generic prompting approaches, GPT Pilot uses specialized, versioned prompt templates for each agent type, enabling domain-specific optimization and systematic improvement of agent behavior.

via “prompt composition strategy selection and technique combination”

Strategies and tactics for getting better results from large language models.

Unique: Provides empirically-grounded guidance on combining prompt techniques based on OpenAI's production experience, including analysis of technique interactions and performance tradeoffs

vs others: More practical than academic papers on prompt engineering, but less automated than frameworks like DSPy that programmatically compose and optimize prompt strategies

via “prompt engineering application use-case library”

Guide and resources for prompt engineering.

via “prompt-optimization-suggestions”

Amplify your workflow with the best prompts.

Unique: Uses LLMs to analyze and suggest improvements to other prompts, creating a meta-layer of prompt engineering assistance

vs others: Provides automated, contextual suggestions vs. static prompt engineering guides or manual expert review

via “prompt engineering technique instruction with interactive examples”

Anthropic's educational courses.

Unique: Combines theoretical prompt engineering principles with executable Jupyter notebooks that learners run against live Claude API, creating immediate feedback loops where prompt modifications produce observable output changes. Organized as a progressive curriculum where each technique builds on prior knowledge rather than standalone reference material.

vs others: More hands-on and structured than blog posts or documentation because learners execute real prompts and observe results directly, and more comprehensive than single-technique tutorials because it covers the full spectrum of core techniques in a coherent learning sequence

via “prompt-engineering-interface”

via “prompt-engineering-abstraction”

via “prompt engineering template library with iterative refinement ui”

Unique: Provides a curated, versioned template library with real-time preview and parameter controls, whereas ChatGPT offers no built-in prompt templates or refinement UI. Templates include metadata (difficulty, format, examples) and integrate with conversation history for contextual suggestions.

vs others: Reduces prompt engineering friction for non-technical users by providing working examples and iterative refinement UI, whereas ChatGPT requires manual prompt crafting from scratch.