Best AI tools for< Deploy Prompts >

PromptPerfect is an AI prompt generator and optimizer that helps users generate high-quality prompts for various purposes. It uses advanced algorithms to analyze input data and provide tailored prompts that are engaging and effective. With its user-friendly interface, users can easily create prompts for writing, brainstorming, presentations, and more. Whether you are a writer, student, or professional, PromptPerfect can enhance your creativity and productivity by providing personalized prompts to inspire your work.

PromptPoint Playground is an AI tool designed to help users design, test, and deploy prompts quickly and efficiently. It enables teams to create high-quality LLM outputs through automatic testing and evaluation. The platform allows users to make non-deterministic prompts predictable, organize prompt configurations, run automated tests, and monitor usage. With a focus on collaboration and accessibility, PromptPoint Playground empowers both technical and non-technical users to leverage the power of large language models for prompt engineering.

Lazy AI is a platform that enables users to build full stack web applications 10 times faster by utilizing AI technology. Users can create and modify web apps with prompts and deploy them to the cloud with just one click. The platform offers a variety of features including AI Component Builder, eCommerce store creation, Crypto Arbitrage Scraper, Text to Speech Converter, Lazy Image to Video generation, PDF Chatbot, and more. Lazy AI aims to streamline the app development process and empower users to leverage AI for various tasks.

Lazy AI is an AI tool that enables users to quickly build and modify web apps with prompts and deploy them to the cloud with just one click. Users can create various applications such as customer portals, API endpoints for AI text summarization, metrics dashboards, web scrapers, chatbots, and discord bots. The platform offers a wide range of template categories and tools for automation, data mining, AI agents, dashboards, reporting, and more. Users can also access reusable templates from the Lazy AI community to streamline their development process.

Azure Static Web Apps is a platform provided by Microsoft Azure for building and deploying modern web applications. It allows developers to easily host static web content and serverless APIs with seamless integration to popular frameworks like React, Angular, and Vue. With Azure Static Web Apps, developers can quickly set up continuous integration and deployment workflows, enabling them to focus on building great user experiences without worrying about infrastructure management.

Outfit AI is an AI tool that enables users to design and deploy AI models or workflows as user-ready applications in minutes. It allows users to create custom user interfaces for their AI-powered apps by dropping in an API key from Replicate or Hugging Face. With Outfit AI, users can have creative control over the design of their apps, build complex workflows without any code, and optimize prompts for better performance. The tool aims to help users launch their models faster, save time, and enhance their AI applications with a built-in product copilot.

Contentable.ai is a platform for comparing multiple AI models, rapidly moving from prototyping to production, and management of your custom AI solutions across multiple vendors. It allows users to test multiple AI models in seconds, compare models side-by-side across top AI providers, collaborate on AI models with their team seamlessly, design complex AI workflows without coding, and pay as they go.

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Vellum AI is an AI platform that supports using Microsoft Azure hosted OpenAI models. It offers tools for prompt engineering, semantic search, prompt chaining, evaluations, and monitoring. Vellum enables users to build AI systems with features like workflow automation, document analysis, fine-tuning, Q&A over documents, intent classification, summarization, vector search, chatbots, blog generation, sentiment analysis, and more. The platform is backed by top VCs and founders of well-known companies, providing a complete solution for building LLM-powered applications.

Plumb is a no-code, node-based builder that empowers product, design, and engineering teams to create AI features together. It enables users to build, test, and deploy AI features with confidence, fostering collaboration across different disciplines. With Plumb, teams can ship prototypes directly to production, ensuring that the best prompts from the playground are the exact versions that go to production. It goes beyond automation, allowing users to build complex multi-tenant pipelines, transform data, and leverage validated JSON schema to create reliable, high-quality AI features that deliver real value to users. Plumb also makes it easy to compare prompt and model performance, enabling users to spot degradations, debug them, and ship fixes quickly. It is designed for SaaS teams, helping ambitious product teams collaborate to deliver state-of-the-art AI-powered experiences to their users at scale.

Narrow AI is an AI application that autonomously writes, monitors, and optimizes prompts for any model, enabling users to ship AI features 10x faster at a fraction of the cost. It streamlines the workflow by allowing users to test new models in minutes, compare prompt performance, and deploy on the optimal model for their use case. Narrow AI helps users maximize efficiency by generating expert-level prompts, adapting prompts to new models, and optimizing prompts for quality, cost, and speed.

Langtail is a platform that helps developers build, test, and deploy AI-powered applications. It provides a suite of tools to help developers debug prompts, run tests, and monitor the performance of their AI models. Langtail also offers a community forum where developers can share tips and tricks, and get help from other users.

GptSdk is an AI tool that simplifies incorporating AI capabilities into PHP projects. It offers dynamic prompt management, model management, bulk testing, collaboration chaining integration, and more. The tool allows developers to develop professional AI applications 10x faster, integrates with Laravel and Symfony, and supports both local and API prompts. GptSdk is open-source under the MIT License and offers a flexible pricing model with a generous free tier.

Dora is an AI-powered platform that enables users to create 3D animated websites without the need for coding. It caters to designers, freelancers, and creative professionals who seek to design visually captivating websites effortlessly. With Dora, users can craft mesmerizing 3D and animated visuals that are responsive and seamlessly translate across devices. The platform is designed for professionals who prioritize design aesthetics and offers a no-code experience for those transitioning from other design tools. Dora leverages advanced AI algorithms to generate, customize, and deploy stunning landing pages, revolutionizing the web design process.

Zyfo.ai is an AI-powered website generator that allows users to easily create custom websites without the need for templates. It generates layouts, images, and content based on user prompts, making website creation quick and efficient. With features like lead generation, restaurant website management, newsletter integration, and contact form creation, Zyfo.ai offers a comprehensive solution for individuals and businesses looking to establish an online presence. The platform is designed to save time, offer low pricing, provide access to stock photos, optimize for search engines, and ensure mobile-friendliness, all while delivering an authentic website feel with custom domains.

Freeplay is a tool that helps product teams experiment, test, monitor, and optimize AI features for customers. It provides a single pane of glass for the entire team, lightweight developer SDKs for Python, Node, and Java, and deployment options to meet compliance needs. Freeplay also offers best practices for the entire AI development lifecycle.

Wordware is an AI toolkit that empowers cross-functional teams to build reliable high-quality agents through rapid iteration. It combines the best aspects of software with the power of natural language, freeing users from traditional no-code tool constraints. With advanced technical capabilities, multiple LLM providers, one-click API deployment, and multimodal support, Wordware offers a seamless experience for AI app development and deployment.

Dify is an open-source platform for building AI applications that combines Backend-as-a-Service and LLMOps to streamline the development of generative AI solutions. It integrates support for mainstream LLMs, an intuitive Prompt orchestration interface, high-quality RAG engines, a flexible AI Agent framework, and easy-to-use interfaces and APIs. Dify allows users to skip complexity and focus on creating innovative AI applications that solve real-world problems. It offers a comprehensive, production-ready solution with a user-friendly interface.

MAGE is a GPT web app generator that allows users to create custom web applications using natural language prompts. With MAGE, users can quickly and easily build web applications without having to write any code. MAGE is powered by OpenAI's GPT-3 technology, which allows it to understand and generate human-like text. This makes it easy for users to create web applications that are both powerful and user-friendly.

H2O.ai is an AI platform that offers a convergence of the world's best predictive and generative AI solutions. It provides end-to-end GenAI platform for air-gapped, on-premises, or cloud VPC deployments, allowing users to own every part of the stack, including data and prompts. With features like h2oGPTe, h2oGPT, H2O Danube3, H2OVL Mississippi, H2O Eval Studio, and more, H2O.ai empowers users to customize, deploy, and share AI models and applications across various industries and use cases. The platform is known for democratizing AI with automated machine learning and open-source distributed machine learning solutions.

Langfuse is a powerful tool that helps you develop, monitor, and test your LLM applications. With Langfuse, you can: * **Develop:** Instrument your app and start ingesting traces to Langfuse, inspect and debug complex logs, and manage, version, and deploy prompts from within Langfuse. * **Monitor:** Track metrics (cost, latency, quality) and gain insights from dashboards & data exports, collect and calculate scores for your LLM completions, run model-based evaluations, collect user feedback, and manually score observations in Langfuse. * **Test:** Track and test app behaviour before deploying a new version, test expected in and output pairs and benchmark performance before deploying, and track versions and releases in your application. Langfuse is easy to get started with and offers a generous free tier. You can sign up for Langfuse Cloud or deploy Langfuse locally or on your own infrastructure. Langfuse also offers a variety of integrations to make it easy to connect to your LLM applications.

Parea AI provides a SDK to evaluate & monitor AI applications. It allows users to test, evaluate, and monitor their AI models by defining and running experiments. The SDK also enables logging and observability for AI applications, as well as deploying prompts to facilitate collaboration between engineers and subject-matter experts. Users can automatically log calls to OpenAI and Anthropic, create hierarchical traces of their applications, and deploy prompts for integration into their applications.

Alice is an agentic workflow framework that integrates task execution and intelligent chat capabilities. It provides a flexible environment for creating, managing, and deploying AI agents for various purposes, leveraging a microservices architecture with MongoDB for data persistence. The framework consists of components like APIs, agents, tasks, and chats that interact to produce outputs through files, messages, task results, and URL references. Users can create, test, and deploy agentic solutions in a human-language framework, making it easy to engage with by both users and agents. The tool offers an open-source option, user management, flexible model deployment, and programmatic access to tasks and chats.

The Prompt Engineering Holy Grail repository is a curated resource for prompt engineering enthusiasts, providing essential resources, tools, templates, and best practices to support learning and working in prompt engineering. It covers a wide range of topics related to prompt engineering, from beginner fundamentals to advanced techniques, and includes sections on learning resources, online courses, books, prompt generation tools, prompt management platforms, prompt testing and experimentation, prompt crafting libraries, prompt libraries and datasets, prompt engineering communities, freelance and job opportunities, contributing guidelines, code of conduct, support for the project, and contact information.

This starter project is designed to process `.mdx` files in the `pages` directory to use as custom context within OpenAI Text Completion prompts. It involves building a custom ChatGPT style doc search powered by Next.js, OpenAI, and Supabase. The project includes steps for pre-processing knowledge base, storing embeddings in Postgres, performing vector similarity search, and injecting content into OpenAI GPT-3 text completion prompt.

The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.

Latitude is an open-source prompt engineering platform that helps developers and product teams build AI features with confidence. It simplifies prompt management, aids in testing AI responses, and provides detailed analytics on request performance. Latitude offers collaborative prompt management, support for advanced features, version control, API and SDKs for integration, observability, evaluations in batch or real-time, and is community-driven. It can be deployed on Latitude Cloud for a managed solution or self-hosted for control and customization.

The LLM Twin Course is a free, end-to-end framework for building production-ready LLM systems. It teaches you how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices. The course is split into 11 hands-on written lessons and the open-source code you can access on GitHub. You can read everything and try out the code at your own pace.

The ChatGPT Telegram Bot is a powerful Telegram bot that utilizes various GPT models, including GPT3.5, GPT4, GPT4 Turbo, GPT4 Vision, DALL·E 3, Groq Mixtral-8x7b/LLaMA2-70b, and Claude2.1/Claude3 opus/sonnet API. It enables users to engage in efficient conversations and information searches on Telegram. The bot supports multiple AI models, online search with DuckDuckGo and Google, user-friendly interface, efficient message processing, document interaction, Markdown rendering, and convenient deployment options like Zeabur, Replit, and Docker. Users can set environment variables for configuration and deployment. The bot also provides Q&A functionality, supports model switching, and can be deployed in group chats with whitelisting. The project is open source under GPLv3 license.

BentoDiffusion is a BentoML example project that demonstrates how to serve and deploy diffusion models in the Stable Diffusion (SD) family. These models are specialized in generating and manipulating images based on text prompts. The project provides a guide on using SDXL Turbo as an example, along with instructions on prerequisites, installing dependencies, running the BentoML service, and deploying to BentoCloud. Users can interact with the deployed service using Swagger UI or other methods. Additionally, the project offers the option to choose from various diffusion models available in the repository for deployment.

Suno Music Generator is an unofficial website based on NextJS for suno.ai music generation. It can generate songs you want in about a minute by using user input prompts. The project reverse engineers suno.ai's song generation API using JavaScript and integrates payment with Lemon Squeezy. It also includes token update and maintenance features to prevent token expiration. Users can deploy the project with Vercel and follow a quick start guide to set up the environment and run the project locally. The project acknowledges Suno AI, NextJS, Clerk, node-postgres, tailwindcss, Lemon Squeezy, and aiwallpaper. Users can contact the developer via Twitter and support the project by buying a coffee.

A Telegram LLM bot that allows users to deploy their own Telegram bot in 3 simple steps by creating a flow function, configuring access to the Telegram bot, and connecting to an LLM backend. Users need to sign into flows.network, have a bot token from Telegram, and an OpenAI API key. The bot can be customized with ChatGPT prompts and integrated with OpenAI and Telegram for various functionalities.

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

Contoso Chat is a Python sample demonstrating how to build, evaluate, and deploy a retail copilot application with Azure AI Studio using Promptflow with Prompty assets. The sample implements a Retrieval Augmented Generation approach to answer customer queries based on the company's product catalog and customer purchase history. It utilizes Azure AI Search, Azure Cosmos DB, Azure OpenAI, text-embeddings-ada-002, and GPT models for vectorizing user queries, AI-assisted evaluation, and generating chat responses. By exploring this sample, users can learn to build a retail copilot application, define prompts using Prompty, design, run & evaluate a copilot using Promptflow, provision and deploy the solution to Azure using the Azure Developer CLI, and understand Responsible AI practices for evaluation and content safety.

The End-to-End LLM Bootcamp is a comprehensive training program that covers the entire process of developing and deploying large language models. Participants learn to preprocess datasets, train models, optimize performance using NVIDIA technologies, understand guardrail prompts, and deploy AI pipelines using Triton Inference Server. The bootcamp includes labs, challenges, and practical applications, with a total duration of approximately 7.5 hours. It is designed for individuals interested in working with advanced language models and AI technologies.

Dialog is an API-focused tool designed to simplify the deployment of Large Language Models (LLMs) for programmers interested in AI. It allows users to deploy any LLM based on the structure provided by dialog-lib, enabling them to spend less time coding and more time training their models. The tool aims to humanize Retrieval-Augmented Generative Models (RAGs) and offers features for better RAG deployment and maintenance. Dialog requires a knowledge base in CSV format and a prompt configuration in TOML format to function effectively. It provides functionalities for loading data into the database, processing conversations, and connecting to the LLM, with options to customize prompts and parameters. The tool also requires specific environment variables for setup and configuration.

ChatGPT Next Web is a well-designed cross-platform ChatGPT web UI tool that supports Claude, GPT4, and Gemini Pro models. It allows users to deploy their private ChatGPT applications with ease. The tool offers features like one-click deployment, compact client for Linux/Windows/MacOS, compatibility with self-deployed LLMs, privacy-first approach with local data storage, markdown support, responsive design, fast loading speed, prompt templates, awesome prompts, chat history compression, multilingual support, and more.

Palico AI is a tech stack designed for rapid iteration of LLM applications. It allows users to preview changes instantly, improve performance through experiments, debug issues with logs and tracing, deploy applications behind a REST API, and manage applications with a UI control panel. Users have complete flexibility in building their applications with Palico, integrating with various tools and libraries. The tool enables users to swap models, prompts, and logic easily using AppConfig. It also facilitates performance improvement through experiments and provides options for deploying applications to cloud providers or using managed hosting. Contributions to the project are welcomed, with easy ways to get involved by picking issues labeled as 'good first issue'.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

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