Prompt Engineering Technique Instruction With Interactive 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-technique-progression”

21 Lessons, Get Started Building with Generative AI

Unique: Structures prompt engineering as a learnable skill progression rather than a collection of tips, with explicit before/after examples showing how each technique improves output. Includes code examples that directly integrate with OpenAI/Azure APIs, allowing immediate application in real projects.

vs others: More systematic and teachable than scattered prompt tips found in blogs, yet more practical and immediately applicable than academic papers on prompt design, with direct API integration examples.

via “instruction engineering and constraint-based generation”

22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.

Unique: Provides dedicated Jupyter notebooks isolating instruction engineering as a distinct technique, with examples showing how instruction clarity directly impacts output quality. Includes patterns for constraint specification (output format, length, tone) and negative instructions, with before/after comparisons.

vs others: More actionable than generic prompting advice because it systematically teaches instruction clarity principles with measurable improvements, whereas most guides treat instructions as obvious.

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 technique documentation and pattern library”

总结Prompt&LLM论文，开源数据&模型，AIGC应用

Unique: Organizes prompting techniques into a research-grounded taxonomy that connects empirical papers to practical methodologies, showing how techniques like few-shot learning relate to instruction tuning and in-context learning through shared theoretical foundations rather than treating them as isolated tricks.

vs others: Deeper than prompt engineering guides (e.g., OpenAI docs) by grounding each technique in peer-reviewed research and showing relationships between approaches; more practical than academic surveys by organizing papers by actionable technique rather than chronology.

via “dynamic prompt engineering and few-shot learning”

We’ve been working with automating coding agents in sandboxes as of late. It’s bewildering how poorly standardized and difficult to use each agent varies between each other.We open-sourced the Sandbox Agent SDK based on tools we built internally to solve 3 problems:1. Universal agent API: interact w

Unique: Automatically selects few-shot examples based on task similarity and integrates with agent memory to retrieve successful examples from past executions, reducing manual prompt engineering effort

vs others: More automated than manual few-shot engineering because it uses similarity-based example selection and learns from past successful executions, improving prompts over time without human intervention

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 “interactive jupyter notebook examples for hands-on prompt engineering practice”

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

Unique: Provides executable notebooks integrated within the documentation platform, enabling learners to run examples directly from the guide without setting up separate environments

vs others: More interactive than static documentation because code is executable; more accessible than academic papers because it includes working examples; more practical than tutorials because learners can modify and experiment

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 “system prompt and instruction generation”

Assistant for creating GPT-based assistants.

Unique: Integrates prompt engineering best practices (role clarity, output formatting, constraint specification) into the generation process itself, rather than producing raw text that requires manual refinement. The builder suggests structural improvements and validates that prompts include necessary elements like tone definition and output format specification.

vs others: More comprehensive than simple prompt templates because it generates context-specific prompts tailored to the user's domain, while more practical than hiring prompt engineers by automating the synthesis of best practices into coherent instructions.

via “prompt engineering and parameter tuning interface”

A large list of Google Colab notebooks for generative AI, by [@pharmapsychotic](https://twitter.com/pharmapsychotic).

Unique: Provides interactive parameter tuning with real-time preview and preset templates, lowering the barrier to effective prompt engineering for non-technical users compared to command-line or code-based interfaces

vs others: More intuitive than raw API calls or command-line tools, and more flexible than closed platforms that restrict parameter access

via “prompt engineering and optimization techniques”

A repository of useful data science prompts for ChatGPT.

Unique: Provides meta-level guidance on prompt engineering as a distinct section within the repository, explaining the principles behind the provided templates (role-assumption, task description, input placeholders). Treats prompt engineering as a learnable skill rather than an art.

vs others: More educational than other prompt repositories because it explicitly documents prompt design principles and best practices, enabling users to understand and improve prompts rather than just copy-pasting templates.

via “prompt engineering application use-case library”

Guide and resources for prompt engineering.

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 “agent prompt engineering and instruction design”

A book about building AI agents with tools, memory, planning, and multi-agent systems.

Unique: Treats prompt engineering as a systematic discipline with patterns for role definition, constraint encoding, and output formatting rather than ad-hoc trial-and-error

vs others: More agent-focused than generic prompt engineering guides because it addresses multi-step reasoning, tool use, and error recovery in prompts

via “prompt engineering and in-context learning analysis”


Unique: Provides theoretical grounding for empirical prompt engineering practices, explaining the mechanisms behind why certain techniques work rather than just cataloging tricks — moving prompt engineering from art to science with reproducible principles.

vs others: More rigorous than typical prompt engineering guides that focus on heuristics; more practical than pure theory papers; bridges the gap between academic understanding and practitioner needs.