Free AI Prompt Library - ChatGPT, Claude, Gemini

Claude Prompt for Creating a Software Architecture

Designing a software architecture is a fundamental step that determines the maintainability, scalability, and robustness of a project. Claude excels in this area thanks to its ability to analyze complex technical constraints, propose suitable architectural patterns, and anticipate design issues before they arise in production. Whether you are starting a new project from scratch, refactoring a monolith into microservices, or evaluating different technology options, Claude can support you at every stage of the decision-making process. By providing precise context about your constraints (team, budget, expected traffic, existing stack), you get argued architectural recommendations, component diagrams, structured ADRs (Architecture Decision Records), and concrete migration plans. The iterative approach with Claude allows you to explore multiple alternatives, compare their trade-offs, and converge towards a solution that balances pragmatism and technical excellence. It's like having a senior software architect available at all times to challenge your ideas and enrich your thinking.

Prompt ChatGPT to Create a Chatbot

Creating a chatbot with ChatGPT has become an essential skill for businesses and developers looking to automate customer interactions. Whether you want to set up a technical support assistant, a product recommendation bot, or a conversational agent for your website, ChatGPT provides a powerful foundation for designing intelligent and natural chatbots. Thanks to its contextual understanding and text generation capabilities, it enables creating smooth conversational experiences without requiring months of development. However, the quality of your chatbot directly depends on the precision of your instructions. A well-structured prompt defines the behavior, tone, boundaries, and capabilities of your chatbot. In this guide, we offer optimized prompts for designing a high-performance chatbot with ChatGPT, adapted to different expertise levels. You will discover how to formulate clear instructions to get a chatbot that exactly meets your needs, whether it's a simple FAQ bot or an advanced conversational assistant capable of handling complex scenarios and maintaining context over multiple exchanges.

GitHub Copilot Prompt to Create a Brand Style Guide

GitHub Copilot, the AI coding assistant integrated into code editors, can be a powerful ally for creating a complete and structured brand style guide. Whether you are a front-end developer looking to formalize a project's visual identity, a designer wanting to quickly generate a reference document, or a project manager needing a consistent graphic framework, Copilot helps you produce a detailed style guide covering color palette, typography, spacing, UI components, and logo usage rules. By leveraging its ability to generate structured code (CSS, design tokens, configuration JSON), Copilot transforms your creative intentions into immediately actionable technical specifications for a development team. The prompt approach allows for rapid iteration on design choices while maintaining overall consistency. Here is an optimized prompt to get the most out of GitHub Copilot in creating a professional brand style guide, along with variants adapted to your expertise level.

GitHub Copilot Prompt for Creating Mockups

GitHub Copilot, GitHub's AI-powered code assistant, proves to be a formidable tool for creating mockups directly in your code editor. Instead of juggling between Figma, Sketch, and your IDE, you can now generate functional mockups in HTML/CSS or with frameworks like React and Tailwind CSS, all without leaving VS Code. The major advantage lies in iteration speed: in seconds, Copilot translates your textual description into structured visual components, ready to be refined. Whether you're a front-end developer looking to quickly prototype an interface, a designer wanting to validate a concept in code, or a product manager aiming to communicate a product vision, well-crafted prompts for GitHub Copilot transform your workflow. This 'code-first' approach to mockups ensures your designs are immediately production-ready, eliminating the traditional design-to-code conversion step. On this page, discover the optimal prompt for creating professional mockups with Copilot, its variants according to your level, and best practices to obtain results faithful to your vision.

GitHub Copilot Prompt for Analyzing a Survey

Survey analysis is a crucial step for extracting actionable insights from raw data. GitHub Copilot, with its code generation and contextual understanding capabilities, becomes a powerful ally for automating response processing, identifying statistical trends, and producing relevant visualizations. Whether you work with CSV files exported from Google Forms, Typeform, or SurveyMonkey, Copilot can help you quickly write Python or R code to clean data, calculate distributions, perform cross-tabulations, and generate clear charts. Instead of spending hours manually manipulating spreadsheets, you can describe in natural language what you want to analyze and have Copilot generate the corresponding code. This approach is particularly effective for product teams, UX researchers, and marketing managers who need to quickly transform hundreds of responses into concrete recommendations. The prompt below is designed to maximize the relevance of Copilot's suggestions by clearly structuring your analysis expectations.

GitHub Copilot Prompt for Summarizing a Meeting

GitHub Copilot, initially designed for code assistance, also proves to be a formidable ally for corporate productivity tasks, particularly summarizing meetings. Thanks to its ability to structure information, identify key points, and rephrase complex exchanges, Copilot transforms raw notes or transcriptions into usable professional minutes. Whether you use Copilot in VS Code, in GitHub, or via Copilot Chat, the principle remains the same: provide clear context and precise instructions to obtain a faithful, hierarchical, and action-oriented summary. This page offers an optimized prompt to transform any transcription or set of notes into a structured report with decisions, actions, and owners. You will also discover variants adapted to your level of requirement, answers to frequently asked questions, and additional prompts to go further in your productivity with GitHub Copilot.

GitHub Copilot Prompt for Comparing Offers

GitHub Copilot, the AI-powered coding assistant, is not limited to code generation. It also excels in structured data analysis, particularly comparing commercial, technical, or pricing offers. By leveraging its reasoning and formatting capabilities, you can transform a tedious task into a methodical and repeatable process. Whether you are comparing vendor offers, SaaS subscriptions, business proposals, or technical quotes, GitHub Copilot helps you structure your criteria, weight decision factors, and produce a clear summary. The tool is especially effective when used in a development environment where comparative data can be processed as tables, JSON objects, or Markdown files. This programmatic approach to comparison ensures a thorough analysis without missing criteria, while making the result easily shareable with your team. Discover how to craft the ideal prompt to get precise, structured, and actionable offer comparisons directly from your IDE.

GitHub Copilot Prompt for Analyzing an Annual Report

GitHub Copilot, initially designed as a development assistant, also proves formidable for analyzing complex documents like corporate annual reports. By leveraging its contextual understanding and synthesis capabilities, you can quickly extract key financial indicators, identify strategic trends, and spot risks mentioned in these often voluminous documents. Whether you are a financial analyst, investor, or management student, using GitHub Copilot to dissect an annual report saves hours of tedious reading while structuring the information in an actionable way. The approach involves submitting sections of the report with precise instructions to obtain a methodical analysis covering financial performance, governance, strategy, and risk factors. The prompt below is designed to maximize the relevance and depth of the analysis produced by Copilot.

GitHub Copilot Prompt for Extracting Data Insights

GitHub Copilot, the AI development assistant integrated into IDEs, is a powerful ally for extracting insights from raw data. Whether you work with CSV files, SQL databases, or pandas DataFrames, Copilot can quickly generate exploratory analysis, visualization, and statistical synthesis code. The main challenge lies in prompt formulation: a well-structured prompt allows Copilot to produce relevant analysis code on the first suggestion, without unnecessary iterations. By clearly specifying the data format, the metrics sought, and the desired visualization type, you transform Copilot into a true data analyst capable of detecting trends, anomalies, and hidden correlations in your datasets. This page offers an optimized main prompt and variants adapted to your level, to get the most out of GitHub Copilot in your data analysis workflows.

GitHub Copilot Prompt for Competitive Intelligence

GitHub Copilot, the AI-powered coding assistant from GitHub, is not limited to code completion. Used strategically, it becomes a powerful ally to automate and structure your competitive monitoring. By leveraging its code generation and analysis capabilities, you can create scraping scripts, data collection pipelines, comparative dashboards, and automated alert systems to track your competitors. Whether you want to monitor price changes, analyze rival product features, track customer reviews, or detect market trends, GitHub Copilot significantly accelerates the development of these monitoring tools. The major advantage lies in its ability to quickly generate functional code to query APIs, parse web pages, and structure collected data into actionable formats. This guide provides optimized prompts to get the most out of GitHub Copilot for your competitive intelligence, with variants tailored to each level of technical expertise.

GitHub Copilot Prompt for Analyzing User Feedback

Analyzing user feedback is a crucial step to improve a product, service, or customer experience. Yet manually processing hundreds or thousands of reviews—whether from reviews, support tickets, NPS forms, or social media comments—represents considerable work. GitHub Copilot, thanks to its natural language understanding capabilities integrated directly into your code editor, allows you to automate this analysis with remarkable accuracy. By formulating suitable prompts, you can ask Copilot to categorize sentiments, extract recurring themes, identify priority friction points, and generate actionable summaries for your product teams. Whether you work with CSV files, JSON exports, or raw data copied from a support tool, Copilot transforms your IDE into a true qualitative analysis platform. This approach is particularly useful for developers and product managers who want to integrate feedback analysis directly into their technical workflow, without relying on expensive third-party tools or advanced data science skills.

GitHub Copilot Prompt for HR Data Analysis

HR data analysis has become a strategic lever for companies looking to optimize their human capital management. GitHub Copilot, integrated directly into your development environment, offers valuable assistance for quickly writing data analysis scripts related to human resources: turnover, absenteeism, performance, pay equity, or employee satisfaction. Thanks to its contextual understanding of the code, Copilot can generate data cleaning functions, relevant visualizations, and predictive models tailored to HR issues. Whether you work with Python and pandas, R, or SQL, Copilot significantly accelerates the process of data exploration and transformation. This prompt is designed to guide Copilot to produce a complete, structured, and documented HR analysis pipeline capable of transforming raw data into actionable insights for decision-makers. It is intended for both HR data analysts and developers tasked with building people analytics dashboards.