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Prompt course · 9 lessons · 24 prompts · 1 hour · Beginner

AI for Full-Stack Developers

Learn nine practical prompts for full-stack work, from daily coding and debugging to APIs, databases, security, testing, deployment, and client docs. Each lesson fits a real task you already do.

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What you'll learn

  • Daily coding: Use AI to speed up boilerplate, small functions, and code explanations.
  • Debugging errors: Turn stack traces and logs into plain explanations and likely fixes.
  • Frontend components: Build and repair components faster, from layout to state bugs.
  • Backend APIs: Shape and document endpoints before you write the full implementation.
  • Database work: Write queries, plan migrations, and understand table relationships.
  • Auth and security: Set up authentication and catch common security mistakes in your code.
  • Testing and refactoring: Generate tests, clean old code, and plan integration coverage.
  • Deploy and monitor: Create deployment configs and read performance or error logs.

What's inside

9 lessons · 24 prompts
  1. Before you start · framework course RACE Prompt Framework: Role, Action, Context, ExpectationRACE fits full-stack work because you can specify role, action, context, and expected quality when asking for a secure API endpoint fix.
  2. Start here Nina's Wednesday, two waysA day in the life of a Full-Stack Developer, before and after these prompts.
  3. 01 Lesson 1 · 3 prompts Daily Coding Basics
  4. 02 Lesson 2 · 3 prompts Debugging And Errors
  5. 03 Lesson 3 · 3 prompts Frontend Component Work
  6. 04 Lesson 4 · 2 prompts Backend And APIs
  7. 05 Lesson 5 · 2 prompts Database Queries And Schema
  8. 06 Lesson 6 · 2 prompts Authentication And Security
  9. 07 Lesson 7 · 3 prompts Testing And Refactoring
  10. 08 Lesson 8 · 3 prompts Deployment And Monitoring
  11. 09 Lesson 9 · 3 prompts Planning And Client Docs

About this course

7 topics

Prompting for Full-Stack Developers, Lesson by Lesson

This course teaches you how to use AI prompts in the flow of full-stack development. You will practice on boilerplate, errors, components, APIs, databases, security, tests, deployment, and planning.

Every lesson gives you a clear prompt pattern and a way to check the result. You stay in control, and you learn when to trust the answer and when to dig deeper.

  1. The lessons
    1. Daily Coding Basics: Use AI to speed up everyday coding chores like boilerplate, code explanations, and small functions.
    2. Debugging And Errors: Use AI to make sense of errors and failures from the text or logs you already have.
    3. Frontend Component Work: Use AI to build and fix frontend components faster, from design to state bugs.
    4. Backend And APIs: Use AI to shape and document backend APIs before you write the full implementation.
    5. Database Queries And Schema: Use AI to write queries, plan migrations, and understand database relationships.
    6. Authentication And Security: Use AI to set up authentication and catch common security mistakes in your code.
    7. Testing And Refactoring: Use AI to generate tests, refactor old code, and plan integration coverage.
    8. Deployment And Monitoring: Use AI to create deployment configs and read performance or error logs.
    9. Planning And Client Docs: Use AI to estimate work, document systems, and keep clients or teammates informed.
  2. What the course covers

    The course follows nine lessons in order: daily coding basics, debugging and errors, frontend component work, backend and APIs, database queries and schema, authentication and security, testing and refactoring, deployment and monitoring, and planning and client docs.

    Each lesson is short and practical. You get prompt examples, small details to include, and a simple way to review the output before you use it.

  3. How the lessons connect

    The early lessons help you move faster on everyday chores and errors. Then you move into frontend and backend work, where AI helps you plan structure before writing code.

    Database, security, testing, and deployment lessons build on that foundation. The final lesson ties it together with estimates, system docs, and client updates.

  4. How to use prompts well

    Give your AI tool real context: the language, framework, error text, and what you have already tried. Ask for a short explanation first, then a fix or a draft.

    Always read the answer like a code review. Run the tests, check edge cases, and keep secrets out of the prompt. Good prompts are a starting point, not a final answer.

  5. Who this course is for

    This course is for full-stack developers who want to work faster without cutting corners. It fits people who move between frontend, backend, database, and deployment tasks.

    It is also useful for developers who support clients or teammates and need to explain technical work in plain language.

  6. Safety and privacy for this job

    Full-stack developers touch customer data, API keys, and private business logic. Never paste secrets, passwords, or personal data into an AI tool unless your company has approved that use.

    Use sample data or redact details. Review every suggestion for security holes, and follow your team's rules for code review and deployment.

  7. Your next step

    Pick one lesson that matches a task on your board this week. Try the prompt on a real ticket, then note what worked and what you changed.

    When that feels easy, move to the next lesson. Small, steady practice will change how you work more than a single long session.