Free AI Prompt Library - ChatGPT, Claude, Gemini

ChatGPT Prompt for Generating JavaScript Code

ChatGPT has become an essential tool for JavaScript developers, whether beginners or experienced. Thanks to its ability to understand natural language instructions, it can generate functional JavaScript code in seconds: utility functions, DOM manipulation, API calls, complex algorithms, or interactive components. But the quality of the generated code depends directly on the precision of the prompt used. A vague prompt will yield a generic result, while a structured prompt — specifying context, technical constraints, expected code style, and edge cases to handle — will produce clean, maintainable code ready to integrate into your project. In this guide, you will find a main prompt optimized for generating JavaScript code with ChatGPT, as well as three variants adapted to your level. Whether you want to quickly prototype a feature, learn new ES6+ syntax, or produce production-ready code with error handling and JSDoc typing, these prompts will save you considerable time while helping you improve.

Midjourney Prompt to Generate SQL Queries

Midjourney, renowned for its powerful AI image generation, can be creatively repurposed to produce visuals related to SQL queries. Although Midjourney does not directly generate executable SQL code, it excels at creating visual representations of database structures, relational diagrams, table schemas, and educational illustrations that facilitate understanding and designing SQL queries. By crafting precise prompts, you can obtain elegant entity-relationship diagrams, data flow visualizations, infographics explaining SQL joins, or database architectural schemas. These visuals are particularly useful for technical documentation, team presentations, training materials, and specialized blog posts. The approach involves accurately describing the type of data structure you want to illustrate, using SQL technical vocabulary to guide Midjourney toward relevant and professional results. Whether you are a developer, data architect, or trainer, these prompts will allow you to transform abstract database concepts into clear and impactful visuals.

GitHub Copilot Prompt for Creating a REST API

GitHub Copilot has become an essential assistant for developers looking to accelerate the creation of REST APIs. Thanks to its direct integration into the code editor, it understands the context of your project and generates structured code in real time. Whether you work with Node.js and Express, Python and FastAPI, or Java and Spring Boot, Copilot adapts to your tech stack to produce endpoints conforming to REST conventions. The main challenge lies in formulating the prompt: a well-structured prompt yields a complete API with data validation, error handling, authentication, and documentation. A vague prompt, on the other hand, will produce generic code requiring many adjustments. In this guide, we offer an optimized prompt that fully leverages GitHub Copilot's capabilities to generate a professional, testable, production-ready REST API. You will also discover variants adapted to your experience level as well as practical tips to get the most out of this tool on a daily basis.

DALL-E Prompt for Code Refactoring

DALL-E, OpenAI's image generation model, may seem a surprising choice for code refactoring. Yet visualization is a powerful lever for rethinking software architecture. By generating conceptual diagrams, architecture schemas, or visual representations of data flows, DALL-E enables developers to step back from their codebase and identify refactoring patterns that escape a linear reading of the code. This visual approach is particularly effective for communicating architectural decisions to a team, documenting a complex refactoring before implementing it, or exploring different modular organizations. Rather than refactoring the code directly, DALL-E intervenes upstream: it produces clear visual representations of the current and target states, facilitating the planning and collective validation of structural changes. Combined with code tools like ChatGPT or Claude, DALL-E completes the workflow by bringing the visual dimension often missing in refactoring processes. This page guides you in creating DALL-E prompts that generate useful architectural visualizations for your refactoring projects.

Sora Prompt for Creating Software Architecture

Sora, OpenAI's video generation model, opens up unprecedented perspectives for visualizing software architectures. Where static diagrams struggle to convey the complexity of data flows, microservices interactions, or deployment patterns, Sora enables the creation of dynamic visual animations that bring your systems to life. Whether you are preparing a presentation for non-technical stakeholders, documenting a complex distributed architecture, or wish to illustrate the path of a request through the different layers of your application, Sora transforms your textual descriptions into clear and professional explanatory videos. By combining precise instructions on components, flows, and interactions, you obtain animated visualizations that make the abstract tangible. This guide provides you with optimized prompts for generating visual representations of software architectures with Sora, from simple animated diagrams to complete simulations of a distributed production system.

Perplexity Prompt for Creating a Software Architecture

Perplexity stands out as a particularly powerful tool for software architecture design thanks to its ability to synthesize recent and verified technical sources. Unlike a classic LLM that relies solely on its training data, Perplexity queries the web in real time to integrate the latest best practices, emerging architectural patterns, and community feedback. Whether you are designing a microservices architecture, a modular monolith, or an event-driven system, a well-structured prompt allows you to obtain contextualized recommendations with verifiable sources. The challenge is to provide enough business and technical context for Perplexity to cross-reference relevant information: technology stack, scalability constraints, infrastructure budget, and team skills. By phrasing your request precisely, you transform Perplexity into a true consultant architect capable of proposing component diagrams, reasoned technology choices, and deployment strategies tailored to your specific context.

Mistral Prompt for Generating SQL Queries

Mistral, the language model developed by French startup Mistral AI, excels at generating SQL queries thanks to its fine understanding of data structures and relational database syntax. Whether you work with MySQL, PostgreSQL, SQL Server, or SQLite, a well-crafted prompt yields optimized, properly indexed queries tailored to your schema. The main challenge lies in accurately conveying context: table structures, relationships between entities, data types, and business constraints. Without this information, even the best model will produce generic and potentially inefficient queries. By providing Mistral with a clear schema and precise objectives, you transform an often tedious process into near-instant generation of functional queries. This guide offers tested and refined prompts to get the most out of Mistral in your daily SQL tasks, from simple SELECT statements to complex analytical queries with subqueries, multiple joins, and window functions.

Perplexity Prompt for Generating JavaScript Code

Perplexity stands out from other AI assistants by its unique ability to combine real-time web search and code generation. When you ask it to produce JavaScript code, it doesn't just rely on static knowledge: it consults official documentation, recent GitHub repositories, and current technical discussions to provide you with up-to-date and functional code. This approach is especially valuable in JavaScript, an ecosystem where best practices, browser APIs, and conventions evolve rapidly. Where a classic LLM might suggest outdated syntax or a deprecated method, Perplexity checks its sources and cites the references used. Whether you need a React component, a Node.js utility function, or a vanilla DOM manipulation, crafting a structured prompt with the right technical context ensures a result that is immediately usable. This guide shows you how to get the most out of Perplexity for your JavaScript code generation needs, from simple scripts to complete modular architectures.

Stable Diffusion Prompt for Debugging Code

Stable Diffusion, known for its power in image generation, can become an unexpected ally in the code debugging process. By generating conceptual visualizations — flow diagrams, software architecture representations, error schematics, or dependency maps — this tool enables developers to better understand their code structure and visually identify failure points. Instead of reading hundreds of lines of logs, imagine producing a clear illustration of your program's execution path, highlighting bottlenecks and infinite loops. Stable Diffusion excels at creating technical visual representations when the prompt is properly structured: by specifying the desired diagram type, the programming language involved, and the nature of the bug being hunted, you obtain images that serve as powerful thinking aids. This visual approach to debugging perfectly complements traditional tools like debuggers and profilers, offering a different perspective on complex problems. Whether you're facing memory leaks, logic errors, or concurrency issues, a well-crafted prompt transforms Stable Diffusion into a formidably effective visual diagnostic tool.

Perplexity Prompt for Generating SQL Queries

Perplexity stands out from traditional search engines by its ability to synthesize technical information from multiple reliable sources. For generating SQL queries, this tool becomes a true development assistant: it can analyze your database schema, understand your business intent, and produce optimized queries based on the official documentation of PostgreSQL, MySQL, or SQL Server. Unlike a simple code generator, Perplexity contextualizes its responses by citing best practices from Stack Overflow, official documentation, and reputable technical blogs. Whether you need a complex join, an aggregation query with windowing, or performance optimization via indexes, Perplexity provides not only the query but also an explanation of each clause. This educational approach makes it a particularly suitable tool for developers who want to improve their SQL skills while delivering production-ready code. The major advantage lies in its ability to consider the specifics of your DBMS and propose alternatives based on available versions.

Stable Diffusion Prompt for Documenting Code

Stable Diffusion, though primarily known for generating artistic images, can become a powerful tool for creating visuals to accompany code documentation. By generating conceptual diagrams, data flow illustrations, visual representations of software architectures, or thematic icons, Stable Diffusion helps enrich technical documentation that is often dry and hard to grasp. Developers and technical writers can thus produce explanatory schematics, process infographics, or illustrations of abstract concepts like design patterns, data structures, or execution flows. The major advantage lies in production speed: where a graphic designer would take hours to create custom visuals, Stable Diffusion generates illustrations in seconds, allowing rapid iteration until the desired result is achieved. This approach is particularly useful for teams documenting APIs, open-source libraries, or distributed systems, where visual clarity makes all the difference between ignored documentation and documentation developers actually consult.

Perplexity Prompt to Create a Prototype

Perplexity stands out from other AI assistants due to its ability to combine real-time web search with intelligent synthesis. To create a prototype, this dual capability is valuable: Perplexity can simultaneously identify current best practices, suitable frameworks, reusable components, and proven architectures before guiding you through the actual construction. Unlike a traditional search engine that would bury you under links, Perplexity structures information and produces actionable recommendations. Whether you are prototyping a web application, an API, a physical product, or a user interface, the key lies in a prompt that clearly defines the functional scope, technical constraints, and desired fidelity level. A good prompt transforms Perplexity into a prototype architect capable of providing you with a complete action plan, from technology choice to functional code. The recommended approach is to describe your product vision, specify your preferred technical stack, and request a structured deliverable step by step.