Prompt Engineering Playbook: Curriculum and Reusable Prompt Templates for LLM-powered Development¶

Learn prompt engineering end-to-end and apply it with prompt templates for AI-assisted development.

Seven-module curriculum + stack-specific .prompt.md templates that can be used with any coding agent.

Tested environment: Verified in VS Code 1.96+ with GitHub Copilot Pro/Enterprise. The prompt files are plain Markdown and can be adapted for other coding agents.

Safety requirement (sandbox first): Run repository scripts only inside a local Python virtual environment (.venv) to avoid polluting system packages and to reduce risk of accidental environment breakage.

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements-docs.txt -r requirements-dev.txt

For script execution, prefer explicit .venv binaries:

.venv/bin/python scripts/validate-prompt-schema.py
make check

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 Start (60 seconds)
Beta Release Notes
Pick Your Path
Available Stacks
How Prompt Files Work (VS Code Copilot)
Contributing

For AI Agents¶

If you are an AI assistant or automation reading this repository:

Start with llms.txt for the repository purpose and structure contract.
Use GETTING-STARTED.md for installation and usage flow.
Follow CONTRIBUTING.md for formatting, citation, and prompt-file requirements.

Quick Start (60 seconds)¶

For a local/manual setup path (no curl pipe) plus verification steps, see GETTING-STARTED.md.

Option A — 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 — Grab files for one stack:

# 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¶

🎓 I want to learn prompt engineering →¶

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 →¶

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.

📚 I want 20 copy-paste recipes for everyday tasks →¶

Ready-to-use prompts for writing, research, analysis, communication, and decision-making — no programming required. Each recipe is tagged with the prompting patterns it uses.

🔧 I want to set up my project →¶

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¶

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 Inside¶

├── learn/                     🎓 Seven-module curriculum + deep-dive comparisons
│   ├── 00-orientation.md               Story-first on-ramp (no technical background needed)
│   ├── 01-introduction.md
│   ├── 02-core-principles.md
│   ├── 03-patterns.md
│   ├── 04-best-practices.md
│   ├── 05-advanced-patterns.md
│   ├── 06-agentic-patterns.md          Plan-and-execute, reflection loops, multi-agent systems
│   ├── comparisons/                    Chain-of-Thought, ReAct, Few-Shot, cross-model portability
│   └── prompt-examples/                Worked examples for each pattern
│
├── prompts/                   ⚡ Reusable prompt templates
│   ├── shared/                Instructions that apply to ALL stacks
│   ├── python/                Python-specific prompts & instructions
│   ├── react-typescript/      React + TypeScript prompts & instructions
│   ├── react-fastapi/         Full-stack React + FastAPI prompts
│   └── nodejs-typescript/     Node.js + TypeScript prompts & instructions
│
├── scripts/                   🔧 Setup helper scripts
│   ├── python/setup.sh
│   ├── react-typescript/setup.sh
│   ├── react-fastapi/setup.sh
│   └── nodejs-typescript/setup.sh
│
├── GETTING-STARTED.md         How to install and use these templates
├── CONTRIBUTING.md            Guidelines for contributors
├── CHANGELOG.md               Version history and migration guide
└── references.md              Bibliography (APA, with DOIs)

Available Stacks¶

Stack	Instructions	Prompts	Setup Script
Python	copilot-instructions.md	7 prompts	`setup.sh --stack python` (see GETTING-STARTED.md)
React + TypeScript	copilot-instructions.md	8 prompts	`setup.sh --stack react-typescript` (see GETTING-STARTED.md)
React + FastAPI	copilot-instructions.md	3 prompts	`setup.sh --stack react-fastapi` (see GETTING-STARTED.md)
Node.js + TypeScript	copilot-instructions.md	4 prompts	`setup.sh --stack nodejs-typescript` (see GETTING-STARTED.md)

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 — 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/
├── .github/
│   ├── copilot-instructions.md    ← Always active (style, conventions, tooling)
│   └── prompts/
│       ├── create-feature.prompt.md   ← Invoke with /create-feature in Copilot Chat
│       ├── review-code.prompt.md      ← Invoke with /review-code
│       └── ...

The YAML frontmatter mode: 'agent' enables Copilot to read files, run commands, and iterate autonomously. See GETTING-STARTED.md for the full walkthrough.

Contributing¶

Contributions are welcome — whether it's fixing a typo, adding an exercise, or creating prompts for a new stack. See CONTRIBUTING.md for guidelines, commit conventions, and review checklists.

License¶

This project is licensed under the MIT License. See 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 for the bibliography. Machine-readable citation and archival metadata are also provided in CITATION.cff and .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:

@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 — specifically Anthropic Claude and Google Gemini models — 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.