Design, build, and deploy apps with an AI coding agent in an integrated web development environment. 

Build and share two applications—a website performance analyzer and a voting app—while using an AI coding agent to debug, customize, and strengthen your coding skills. Build and share two applications—a website performance analyzer and a voting app—while using an AI coding agent to debug, customize, and strengthen your coding skills.Learn the principles of agentic code development and skills to effectively build, host, and share your apps with Replit coding agents and assistants. Learn the principles of agentic code development and skills to effectively build, host, and share your apps with Replit coding agents and assistants.Use product requirement documents, wireframes, and good prompting practices to prototype, debug, and iterate your applications. Use product requirement documents, wireframes, and good prompting practices to prototype, debug, and iterate your applications._Learn to build and host applications with an AI agent in Vibe Coding 101 with Replit, built in partnership with Replit and taught by Michele Catasta, President, and Matt Palmer, Head of Developer Relations.Coding agents are changing how we write code. “Vibe coding” is a growing practice where you lean into letting a coding agent do most of the heavy lifting while you focus on the architecture and features of your application. But effective vibe coding isn’t just about one-shot prompting, accepting all recommendations, and hoping for the best. It involves structuring your work, refining your prompts, and using frameworks that lead to cleaner, more efficient code.In this course, you’ll use Replit’s cloud environment— with an integrated code editor, package manager, and deployment tools—to build and deploy two web applications with the help of an AI coding agent. Along the way, you’ll learn strategies for working effectively with agents and improve your development skills in the process.In detail, you’ll:Understand principles of agentic code development such as being precise, giving agents one task at a time, making prompts specific, keeping projects tidy, starting with fresh sessions for each new feature, and adopting patience while debugging.Learn how to get started with Replit, and understand fundamental concepts behind effective AI collaboration including a “five-skill framework” in vibe coding of thinking, using frameworks, checkpoints, debugging, and providing context.Create a product requirement document (PRD) and wireframe for your agent to build a prototype of a website performance analyzer.Use the agent to make your prototype more visually appealing, switch to assistant mode to customize your applications with screenshots and prompts, and deploy your application so others can access it online.Build a head-to-head national park ranking app, from a sample dataset, with voting capabilities and persistent data storage.Refine your app by integrating a complete dataset, and ask the assistant to recap and explain what it built to find room for improvement and reinforce your learning.By the end of this course, you’ll have a solid foundation in building with coding agents—and a process you can use to keep vibe coding effectively._Anyone can join! It’s helpful to have some coding experience and some background in prompting LLMs.To complete the course you will need to signup for a free Replit account here.

Vibe Coding 101 with Replit

Learn how to prompt an LLM to help improve, debug, understand, and document your code. Use LLMs to simplify your code and enhance productivity.

Use LLMs to simplify your code and become a more productive software engineer Use LLMs to simplify your code and become a more productive software engineerReduce technical debt by explaining and documenting a complex existing code base Reduce technical debt by explaining and documenting a complex existing code baseGet free access to the PaLM API for use throughout the course Get free access to the PaLM API for use throughout the course_AI pair programming is being rapidly adopted by developers to help with tasks across the tech stack, from catching bugs to quickly inserting entire code snippets. Learn how LLMs can enhance, debug, and document your code in this new course built in collaboration with Google.Get free access to Google’s PaLM API and get hands-on experience that you can apply to your own projects. You’ll learn how to use an LLM in pair programming to:Simplify and improve your codeWrite test cases Debug and refactor your codeExplain and document any complex code written in any coding language _Anyone with basic Python knowledge interested in using LLMs for pair programming.

Pair Programming with a Large Language Model

Automate business workflows with multi-AI agent systems. Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language.

Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language. Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language.Use an open source library, crewAI, to automate repeatable, multi-step tasks like tailoring a resume to a job description; and automate business processes that are typically done by a group of people, like event planning. Use an open source library, crewAI, to automate repeatable, multi-step tasks like tailoring a resume to a job description; and automate business processes that are typically done by a group of people, like event planning.By creating a team of AI agents, you can define a specific role, goal, and backstory for each agent, which breaks down complex multi-step tasks and assigns them to agents that are customized to perform those tasks. By creating a team of AI agents, you can define a specific role, goal, and backstory for each agent, which breaks down complex multi-step tasks and assigns them to agents that are customized to perform those tasks._Learn key principles of designing effective AI agents, and organizing a team of AI agents to perform complex, multi-step tasks. Apply these concepts to automate 6 common business processes.Learn from João Moura, founder and CEO of crewAI, and explore key components of multi-agent systems: Role-playing: Assign specialized roles to agents Memory: Provide agents with short-term, long-term, and shared memoryTools: Assign pre-built and custom tools to each agent (e.g. for web search)Focus: Break down the tasks, goals, and tools and assign to multiple AI agents for better performanceGuardrails: Effectively handle errors, hallucinations, and infinite loopsCooperation: Perform tasks in series, in parallel, and hierarchicallyThroughout the course, you’ll work with crewAI, an open source library designed for building multi-agent systems. You’ll learn to build agent crews that execute common business processes, such as: Tailor resumes and interview prep for job applications Research, write and edit technical articles Automate customer support inquiries Conduct customer outreach campaigns Plan and execute events Perform financial analysisBy the end of the course, you will have designed several multi-agent systems to assist you in common business processes, and also studied the key principles of AI agent systems._If you’ve taken some prompt engineering courses, have some familiarity with basic coding, and want to incorporate LLMs in your professional work, then this course is designed for you!

Multi AI Agent Systems with crewAI

Learn a flexible framework to build a variety of complex AI applications.

Learn a flexible framework to Learn how Haystack components, pipelines, and document stores can be used to build custom AI applications.build custom AI applications with components, pipelines, and document stores Learn how Haystack components, pipelines, and document stores can be used to build custom AI applications.Build a RAG pipeline and extend its ability by creating custom components. Build a RAG pipeline and extend its ability by creating custom components.Use conditional branching, self-reflecting agents, and function calling to build complex pipelines and apps. Use conditional branching, self-reflecting agents, and function calling to build complex pipelines and apps._In Building AI Applications with Haystack you will learn a high-level orchestration framework that helps ensure your applications are flexible, extendible, and maintainable, even as the technology stack changes, user needs arise, and new features are added.Using a framework can provide common features out of the box that significantly speeds up the development process. Haystack offers robust and flexible architecture and framework for building AI applications. It manages complexity and helps you focus more on developing your application at a higher level of abstraction.Throughout the course, you will develop several projects, including a RAG app, a news summarization app, a chat agent with function calling, and a self-reflecting agent with loops.What you’ll do: Learn about the core abstractions and unique building blocks of the Haystack framework and see how these elements can be combined for various AI use cases.Build a RAG pipeline by using Haystack components, pipelines, and document stores.Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs.Use conditional routing to create a branching pipeline with a fall back to web-search when the LLM does not have the context needed to fully respond to the user’s query.Build a self-reflecting agent for named entity recognition with a Haystack pipeline that is able to loop using an output validator custom component.Create a chat agent using OpenAI’s function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent’s capabilities.Start building exciting LLM applications and optimizing your development workflow using Haystack._Anyone who has basic Python knowledge and wants to learn to build and customize complex apps using Haystack!

Building AI Applications With Haystack

Learn to build with LLMs by creating a fun interactive game from scratch.

Learn LLM app development by designing and developing a text-based AI game, creating immersive game worlds, characters, and storylines through hierarchical content generation.  Learn LLM app development by designing and developing a text-based AI game, creating immersive game worlds, characters, and storylines through hierarchical content generation. Learn to implement game mechanics using AI to convert text data into structured JSON outputs, enabling features like an inventory detection system. Learn to implement game mechanics using AI to convert text data into structured JSON outputs, enabling features like an inventory detection system.Learn to enforce safety and compliance for AI content creation, and create custom policies using Llama Guard. Learn to enforce safety and compliance for AI content creation, and create custom policies using Llama Guard._Join our new short course, Building an AI-Powered Game, with Together AI and AI Dungeon! Learn from Niki Birkner, Senior Product Manager at Together AI, and Nick Walton, CEO and Co-Founder of Latitude.This course teaches you how to build a LLM-based application by creating your own AI-powered text-based game. You’ll create a world with hierarchical content generation based on a prompt, setting up its core mechanics, and giving it a simple Gradio user interface. By the end of this course, you’ll have a playable text-based game to share with your friends, built from scratch using LLMs. You will be guided through the building blocks of app development using LLMs, including incorporating safeguards using Llama Guard. What you’ll do: Use prompt engineering to create a world with hierarchical content generation so your narrative is consistent and the AI can flesh out your game.Build the first version of your game with a user interface, load the world, and define the core action loop of the game, so you can have a game to play and interact with.Implement safe user-dependent and context-dependent guardrails into your game application with Llama Guard and custom content policy.Learn LLM tool calling by adding story and state components that improve your game’s memory, track inventory, and show progress.  With these techniques, you’ll hone your AI skills by building your own sharable game! _Anyone who has basic Python knowledge and wants to learn how to build with LLMs in a hands-on, fun way!

Building an AI-Powered Game

Learn to build, debug, and deploy applications with an Agentic AI-powered integrated development environment.

Learn how to collaborate with AI agents to streamline coding workflows, generate and modify code across multiple files, and enhance development efficiency. Learn how to collaborate with AI agents to streamline coding workflows, generate and modify code across multiple files, and enhance development efficiency.Understand how AI coding agents combine tool usage, context awareness, and human-action tracking to function effectively and get a deep dive into search and discovery as one of the main challenges of AI coding agents. Understand how AI coding agents combine tool usage, context awareness, and human-action tracking to function effectively and get a deep dive into search and discovery as one of the main challenges of AI coding agents.Use Windsurf to do real-world coding tasks, including debugging JavaScript code, updating an old large codebase, and building a Wikipedia analysis app that retrieves, processes, and visualizes data while learning to manage unexpected AI behavior. Use Windsurf to do real-world coding tasks, including debugging JavaScript code, updating an old large codebase, and building a Wikipedia analysis app that retrieves, processes, and visualizes data while learning to manage unexpected AI behavior._Join our latest course Build Apps with Windsurf’s AI Coding Agents, built in partnership with Windsurf and taught by Anshul Ramachandran.AI-assisted Integrated Development Environments, or IDEs, make coding and development workflows faster, more efficient, and more fun.Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Using an AI coding agent effectively requires a new approach to prompting, iteration, and structuring development tasks.In this course, you’ll learn the inner workings of coding agents and how to use Windsurf to code and build several applications. In details, you’ll:Learn how to get started with Windsurf, and build your first app (a snake game) in a couple of minutes.Look at the evolution of AI coding assistants, from simple code suggestions to agentic systems that collaborate with developers.Use an AI agent to analyze and resolve issues in Javascript code, like debugging test issues and multi-file refactoring.Learn mental models to understand how coding agents combine human-action tracking, tool integration, and context awareness in their agentic coding workflow.Learn the challenges of code search and discovery of AI coding agents and how a multi-step retrieval approach helps overcome them.Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. Use Windsurf to build a Wikipedia data analysis app — retrieving, parsing, and analyzing word frequencies.Enhance the performance of your Wikipedia app by adding caching and while doing so learn how to course-correct when the AI agent produces unexpected results.Learn tips and tricks such as command, autocomplete, @mentions, and explain functionalities to maximize your interactions with the overall AI system as you build a full-stack app to visualize the data of your Wikipedia analysis app.Leverage the multimodal capabilities of the AI agent to increase your development velocity with an example in which you upload a mockup with sketched-out reference UI features to add new functionalities to your app.By the end of this course, you’ll understand agentic coding in-depth and learn to use it to make your development process faster, more efficient, and more enjoyable._It’s helpful for anyone familiar with coding to have a basic understanding of LLM prompting and AI-assisted application development.