AI Solutions & Applications: Design, Automate, and Deploy with No-Code AI

Use Claude Cowork to plan, build, test, refine, and deploy a functional no-code web application.

• Understand how Claude Cowork differs from chat-based AI for application building.  

• Apply plan-first prompting to gather requirements, generate a solution, and refine its output.  

• Build and test a no-code web application through guided and individual assignments.  

• Publish through GitHub and Vercel, test the deployed application, and assess when professional engineering support is needed. 

Build a structured data foundation in Supabase and examine how data quality and access decisions affect AI outputs.

• Create related Supabase tables using appropriate fields, keys, and sample data.  

• Clean and import a synthetic dataset, then compare AI outputs before and after cleaning.  

• Identify sensitive data and apply data-minimization, access-control, and credential-safety principles.  

• Use the Include–Exclude–Justify process to connect appropriately limited data to an AI-enabled application.

Understand how an application’s interface, logic, and data layers work together to support reliable workflows.

• Map an existing application’s frontend, logic, and data layers.  

• Design a record lifecycle using states, transitions, events, triggers, and business rules.  

• Build, acceptance-test, and troubleshoot a stateful workflow.  

• Configure a secure event-based email notification using Resend and server-side credential storage in Supabase.  

Choose, build, test, and strengthen an AI feature for a defined business need.

• Determine when to use rules, pre-built machine learning, or generative AI.  

• Define and connect a prompt-based AI feature within an application.  

• Test normal, edge, and problematic inputs and document how the feature responds.  

• Add a purposeful guardrail and determine where human review should remain in the workflow.

Turn a real business problem into a focused, user-tested AI-enabled prototype.

• Frame the problem and identify an appropriate AI use case within the workflow.  

• Map the user journey and define the MVP scope for an initial version.  

• Build a functional prototype with at least one working AI feature.  

• Test the prototype with a real user and make an evidence-based improvement.

Evaluate where automation adds value, then extend an application with appropriate automation and integrations and prepare it for practical use.

• Evaluate business workflows to determine where to automate, augment with AI, or keep human judgment in the loop.  

• Design appropriate trigger-action workflows and connect tools or systems.  

• Monitor automated workflows to identify and respond to failures. 

• Make responsible rollout decisions and prepare an application for practical use. 

Prepare AI-enabled applications for safer, more reliable use while understanding when additional technical support is required.

• Evaluate AI outputs for reliability, consistency, usefulness, and known limitations.  

• Apply guardrails, fallback strategies, human review, privacy, security, and access controls. 

• Monitor workflows and assess when an application is appropriate for testing, limited rollout, or internal use. 

• Explore agentic concepts and recognize when advanced systems require technical-team involvement.

Yes. All learners who successfully complete No-Code AI: Building AI-Powered Applications will receive a Stanford Online Certificate of Achievement delivered in a digital badge format and verified on the blockchain. The credential recognizes proficiency in the course material. 

No prior programming experience or experience with any specific tool is required. Module 0 provides setup guidance for the core course tools.

The course uses AI-assisted and no-code/low-code workflows so you can focus on requirements, application behavior, testing, and refinement rather than traditional code-first development.

This course is designed for business, product, innovation, operations, program, project, consulting, and entrepreneurial professionals who want to move from using AI tools to developing practical applications, workflows, and prototypes.

The course introduces agentic workflows in a no-code context and covers when to automate, augment with AI, or keep a person in the loop. It does not focus on building advanced agents. You will learn how to recognize when a project needs that level of technical support.

The course uses Claude Cowork, Supabase, GitHub, and Vercel, along with selected supporting services. Before the course begins, you will receive information about required accounts, any subscription requirements, and any paid access provided as part of the course. Module 0 includes step-by-step setup guidance.

Applicable taxes will be calculated and added at checkout in accordance with country and state regulations.