# Prompt Engineering Playbook > A seven-module curriculum + stack-specific prompt templates for AI-assisted development β€” works with any LLM. [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18827631.svg)](https://doi.org/10.5281/zenodo.18827631) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) [![Docs](https://img.shields.io/badge/docs-mkdocs-blue.svg)](https://kunalsuri.github.io/prompt-engineering-playbook/) [![Build](https://img.shields.io/github/actions/workflow/status/kunalsuri/prompt-engineering-playbook/quality-nonmarkdown.yml?branch=main&label=checks)](https://github.com/kunalsuri/prompt-engineering-playbook/actions/workflows/quality-nonmarkdown.yml) **[🌐 View the Documentation Site β†’](https://kunalsuri.github.io/prompt-engineering-playbook/)** > **Tested environment:** Verified in VS Code 1.96+ with GitHub Copilot Pro/Enterprise. Prompt files are plain Markdown and work with any coding agent. --- ## Who This Is For - **For:** developers, contributors, educators, and researchers who want practical prompt-engineering curriculum and reusable prompt templates. - **For:** teams using VS Code + GitHub Copilot who need structured `.prompt.md` workflows. - **Not for:** model training, benchmark leaderboards, or framework-specific SDK implementations. ## Quick Navigation - [Quick Start (60 seconds)](#quick-start-60-seconds) - [Pick Your Path](#pick-your-path) - [What's in This Repo](#whats-in-this-repo) - [Available Stacks](#available-stacks) - [How Prompt Files Work (VS Code Copilot)](#how-prompt-files-work-vs-code-copilot) - [Contributing](#contributing) ## For AI Agents If you are an AI assistant or automation reading this repository: - Start with [llms.txt](https://github.com/kunalsuri/prompt-engineering-playbook/blob/main/llms.txt) for the repository purpose and structure contract. - Use [GETTING-STARTED.md](GETTING-STARTED.md) for installation and usage flow. - Follow [CONTRIBUTING.md](CONTRIBUTING.md) for formatting, citation, and prompt-file requirements. ## Quick Start (60 seconds) > **Safety note:** Run repository scripts inside a Python virtual environment to avoid polluting system packages. > ```bash > python3 -m venv .venv && source .venv/bin/activate > pip install -r requirements-docs.txt -r requirements-dev.txt > ``` For a local/manual setup path (no `curl` pipe) plus verification steps, see [GETTING-STARTED.md](GETTING-STARTED.md#manual-install-no-curl-bash). **Option A β€” Use as a GitHub template:** Click **"Use this template"** at the top of this page to create your own copy with all files included. **Option B β€” Grab files for one stack:** ```bash # Example: set up Python prompts in your project mkdir -p .github/prompts # Base instructions (Copilot reads this automatically) curl -o .github/copilot-instructions.md \ https://raw.githubusercontent.com/kunalsuri/prompt-engineering-playbook/main/prompts/python/copilot-instructions.md # All Python prompt files curl -o .github/prompts/create-feature.prompt.md \ https://raw.githubusercontent.com/kunalsuri/prompt-engineering-playbook/main/prompts/python/prompts/create-feature.prompt.md # Repeat for each prompt file you need, or clone and copy: git clone https://github.com/kunalsuri/prompt-engineering-playbook.git cp -r prompt-engineering-playbook/prompts/python/prompts/*.prompt.md .github/prompts/ ``` --- ## Pick Your Path ### πŸŽ“ [I want to **learn** prompt engineering β†’](learn/README.md) A seven-module curriculum that takes you from first principles through advanced techniques like RAG, adversarial robustness, systematic evaluation, and agentic architectures. Each module includes worked examples and hands-on exercises. No prior prompt engineering experience required. ### ⚑ [I want to **use** prompt templates β†’](prompts/README.md) Copy-paste-ready prompt files for Python, React/TypeScript, React + FastAPI, and Node.js/TypeScript projects. Optimized for VS Code Copilot's agent mode, but the prompt content works with any LLM. Pick your stack, grab the files, and start building. ### πŸ“š [I want **20 copy-paste recipes** for everyday tasks β†’](learn/cookbook.md) Ready-to-use prompts for writing, research, analysis, communication, and decision-making β€” no programming required. Each recipe is tagged with the prompting patterns it uses. ### πŸ”§ [I want to **set up** my project β†’](GETTING-STARTED.md) Step-by-step guide to installing these templates in your own project (with first-class VS Code Copilot integration) and customizing templates for your team. --- ## Learning Path ```mermaid graph TD A[Module 0: Orientation] --> B[Module 1: Introduction] B --> C[Module 2: Core Principles] C --> D[Module 3: Patterns] D --> E[Module 4: Best Practices] E --> F[Module 5: Advanced Patterns] F --> G[Module 6: Agentic Patterns] D -.-> H[Prompt Templates] F -.-> I[Labs & Comparisons] ``` ## What's in This Repo ``` prompt-engineering-playbook/ β”‚ β”œβ”€β”€ learn/ πŸŽ“ Seven-module curriculum β”‚ β”œβ”€β”€ 00-orientation.md ← Story-first on-ramp (no jargon, no code) β”‚ β”œβ”€β”€ 01-06-*.md ← Core modules (Introduction β†’ Agentic Patterns) β”‚ β”œβ”€β”€ comparisons/ ← Research-backed technique comparisons (CoT, ReAct, Few-Shot…) β”‚ β”œβ”€β”€ prompt-examples/ ← Worked examples for each pattern β”‚ β”œβ”€β”€ labs/ ← Six runnable Python experiments + failure gallery β”‚ β”œβ”€β”€ decisions/ ← Architecture Decision Records (why we chose X over Y) β”‚ β”œβ”€β”€ solutions/ ← Reference solutions for all module exercises β”‚ └── *.md ← Guides: cheatsheet, cookbook, glossary, debugging, meta-prompting… β”‚ β”œβ”€β”€ prompts/ ⚑ Reusable prompt templates by stack β”‚ β”œβ”€β”€ python/ ← 7 prompts + copilot-instructions.md β”‚ β”œβ”€β”€ react-typescript/ ← 8 prompts + copilot-instructions.md β”‚ β”œβ”€β”€ react-fastapi/ ← 3 prompts + copilot-instructions.md β”‚ β”œβ”€β”€ nodejs-typescript/ ← 4 prompts + copilot-instructions.md β”‚ β”œβ”€β”€ shared/ ← Evaluation template, README base, JSON schema β”‚ └── user-prompts/ ← Generic everyday prompts (non-coding) β”‚ β”œβ”€β”€ scripts/ πŸ”§ Repo automation & per-stack setup helpers β”‚ β”œβ”€β”€ setup.sh ← Project setup script β”‚ β”œβ”€β”€ check-citations.py ← Validates all [CitationKey] references β”‚ β”œβ”€β”€ check-lab-sync.py ← Ensures lab .py and .ipynb files stay in sync β”‚ β”œβ”€β”€ lint-*.sh ← Linters for prompt frontmatter and copilot instructions β”‚ β”œβ”€β”€ validate-prompt-schema.py ← JSON Schema validation for .prompt.md files β”‚ β”œβ”€β”€ run-notebook-smoke.py ← Smoke-tests all Jupyter notebooks β”‚ └── {python,react-typescript,react-fastapi,nodejs-typescript}/setup.sh β”‚ β”œβ”€β”€ .github/ πŸ€– CI workflows, issue templates, Copilot instructions β”œβ”€β”€ assets/ 🎨 CSS and favicon for the documentation site β”œβ”€β”€ docs_src/ πŸ“Ž Symlinks used by MkDocs to build the docs site β”‚ β”œβ”€β”€ README.md ← You are here β”œβ”€β”€ GETTING-STARTED.md ← Installation and first-use walkthrough β”œβ”€β”€ CONTRIBUTING.md ← Contributor guidelines and commit conventions β”œβ”€β”€ CHANGELOG.md ← Version history β”œβ”€β”€ ROADMAP.md ← Planned features and future work β”œβ”€β”€ ARCHITECTURE.md ← Deep-dive architecture documentation β”œβ”€β”€ DEVELOPMENT_WORKFLOW.md ← Step-by-step developer workflows β”œβ”€β”€ CONTRIBUTING_AI.md ← AI-agent-specific contribution guide β”œβ”€β”€ AGENT.md ← General AI agent context file β”œβ”€β”€ CLAUDE.md ← Claude Code context file β”œβ”€β”€ REPOSITORY_MAP.md ← Full navigable file inventory β”œβ”€β”€ TECHNICAL-REPORT.md ← Technical report on the playbook β”œβ”€β”€ BETA-RELEASE-NOTES.md ← Beta-specific release notes β”œβ”€β”€ SECURITY.md ← Security policy β”œβ”€β”€ CODE_OF_CONDUCT.md ← Community code of conduct β”œβ”€β”€ references.md ← Bibliography (APA, with DOIs) β”œβ”€β”€ llms.txt ← Machine-readable repo summary for LLMs β”œβ”€β”€ mkdocs.yml ← Documentation site configuration β”œβ”€β”€ requirements-docs.txt ← Docs build dependencies β”œβ”€β”€ requirements-dev.txt ← Dev/CI dependencies └── Makefile ← Common dev tasks (make sync, make build, make check…) ``` --- ## Available Stacks | Stack | Instructions | Prompts | Setup Script | |-------|-------------|---------|-------------| | **Python** | [copilot-instructions.md](prompts/python/copilot-instructions.md) | [7 prompts](prompts/python/prompts/README.md) | `setup.sh --stack python` (see [GETTING-STARTED.md](GETTING-STARTED.md#step-3-copy-templates-into-your-project)) | | **React + TypeScript** | [copilot-instructions.md](prompts/react-typescript/copilot-instructions.md) | [8 prompts](prompts/react-typescript/prompts/README.md) | `setup.sh --stack react-typescript` (see [GETTING-STARTED.md](GETTING-STARTED.md#step-3-copy-templates-into-your-project)) | | **React + FastAPI** | [copilot-instructions.md](prompts/react-fastapi/copilot-instructions.md) | [3 prompts](prompts/react-fastapi/prompts/README.md) | `setup.sh --stack react-fastapi` (see [GETTING-STARTED.md](GETTING-STARTED.md#step-3-copy-templates-into-your-project)) | | **Node.js + TypeScript** | [copilot-instructions.md](prompts/nodejs-typescript/copilot-instructions.md) | [4 prompts](prompts/nodejs-typescript/prompts/README.md) | `setup.sh --stack nodejs-typescript` (see [GETTING-STARTED.md](GETTING-STARTED.md#step-3-copy-templates-into-your-project)) | Each stack includes a `copilot-instructions.md` (base rules Copilot follows automatically) and task-specific `.prompt.md` files (invoked on demand via Copilot Chat). The prompt content itself is model-agnostic β€” you can paste it into ChatGPT, Claude, Gemini, or any other LLM. --- ## How Prompt Files Work (VS Code Copilot) When you place files in your project's `.github/` directory, VS Code Copilot picks them up automatically: ``` your-project/ β”œβ”€β”€ .github/ β”‚ β”œβ”€β”€ copilot-instructions.md ← Always active (style, conventions, tooling) β”‚ └── prompts/ β”‚ β”œβ”€β”€ create-feature.prompt.md ← Invoke with /create-feature in Copilot Chat β”‚ β”œβ”€β”€ review-code.prompt.md ← Invoke with /review-code β”‚ └── ... ``` The YAML frontmatter `mode: 'agent'` enables Copilot to read files, run commands, and iterate autonomously. See [GETTING-STARTED.md](GETTING-STARTED.md) for the full walkthrough. --- ## Contributing Contributions are welcome β€” whether it's fixing a typo, adding an exercise, or creating prompts for a new stack. See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines, commit conventions, and review checklists. ## License This project is licensed under the MIT License. See [LICENSE](LICENSE) for details. ## ✍️ How to Cite & AI Usage ### Citation details If you use this framework to structure your research, paper framing, or methodology curriculum, please cite it using the following format and check [references.md](references.md) for the bibliography. Machine-readable citation and archival metadata are also provided in [CITATION.cff](https://github.com/kunalsuri/prompt-engineering-playbook/blob/main/CITATION.cff) and [.zenodo.json](https://github.com/kunalsuri/prompt-engineering-playbook/blob/main/.zenodo.json). **APA Format:** > Suri, K. (2026). *Prompt Engineering Playbook: Curriculum and Reusable Prompt Templates for LLM-powered Development (v0.1.0-beta)*. Zenodo. https://doi.org/10.5281/zenodo.18827631 **BibTeX:** ```bibtex @software{suri2026promptengineering, author = {Suri, Kunal}, title = {Prompt Engineering Playbook: Curriculum and Reusable Prompt Templates for LLM-powered Development}, year = {2026}, version = {v0.1.0-beta}, publisher = {Zenodo}, doi = {10.5281/zenodo.18827631}, url = {https://doi.org/10.5281/zenodo.18827631}, } ``` ---
AI Transparency and Responsible Use * **Responsible Use of AI:** - **Data Privacy:** Prioritize local open-weight models for processing sensitive or educational data to ensure data sovereignty. - **Human Validation:** All AI-generated outputs are validated before integration into teaching, research, or decision-making workflows. - **Compliance:** This project aligns with EU Guidance on Responsible Use of Generative AI in Research. * **Coding:** This project was developed with assistance from the following AI tools: GitHub Copilot (Pro/Enterprise), Google's Antigravity IDE, Local Open-Weight Models (via Ollama in VS Code, e.g., Mistral). These tools were used primarily for code generation, completion, and debugging. All AI-assisted code was independently reviewed, tested, and refined by the authors. The authors take full responsibility for the correctness, security, and integrity of the codebase. * **Writing & Ideation:** Large language model (LLM) tools β€” specifically Anthropic Claude and Google Gemini models β€” were used to support brainstorming, structural organization, and language refinement during the writing process. All underlying arguments, intellectual contributions, and conclusions originate with the authors. All AI-assisted material was critically reviewed and substantially revised by the authors, who take full responsibility for the accuracy, originality, and integrity of the published content.
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