Training course · Bolt · API Development

Bolt for API Developers

Full-stack prototypes right in the browser: generate web apps from prompts with Bolt, run them in WebContainers and deploy them.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects Bolt with the everyday processes of API Development. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Bolt by StackBlitz generates working full-stack applications from prompts and runs them straight away in the browser. WebContainers make this possible: a Node.js runtime runs entirely inside the browser tab, complete with package installation, dev server and terminal. In this one-day course you learn how to build prototypes quickly with React, Vue, Svelte, Next.js, Astro or Node.js backends, how to structure prompts and project instructions, and how to steer the agent precisely rather than letting it rewrite large parts of your app unchecked.

The course combines fast results with sound engineering practice. You read and edit the generated code in the built-in editor, fix errors using the terminal and console, connect databases and authentication, and export projects to GitHub or as an archive. You then publish your application and learn how to keep token consumption, context size and project scope under control. By the end you know exactly which projects Bolt gets to a clickable result fastest, and at what point moving to a local development environment makes sense.

APIs form the backbone of modern software landscapes and are increasingly the interface through which AI agents interact with systems. AI coding assistants speed up the entire API lifecycle, from drafting an OpenAPI or AsyncAPI specification to generating server stubs, clients and contract tests, through to documentation and versioning. At the same time, demands for consistency, security and backward compatibility keep rising, because APIs are consumed by many teams, partners and automated agents. In this course you learn to practise API-first development with AI support, enforce design guidelines automatically, catch breaking changes early and expose existing interfaces to AI agents through the Model Context Protocol or tool definitions.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Backend developers building REST, GraphQL or gRPC interfaces
  • API architects and owners of API governance
  • Integration developers connecting partners and platforms
  • Platform engineering teams running API gateways and developer portals
  • Developers making company data available to AI agents

Typical challenges we address

  • Inconsistent API design across teams with differing conventions for errors, pagination and versioning
  • Specification, implementation and documentation drift apart
  • Breaking changes are only discovered by consumers
  • Securing authentication, authorisation and rate limiting as users and agent traffic grow

What you will be able to do

  • You can explain how WebContainers work and judge which projects run well in Bolt.
  • You generate a full-stack prototype with frontend, backend and database using structured prompts.
  • You debug issues efficiently via terminal, console and precise feedback to the agent.
  • You publish your application and export the code to GitHub for further development.
  • Practise API-first development with AI support, from specification to clients, tests and documentation.
  • Automate design governance, contract testing and breaking-change detection in your pipeline.
  • Make existing APIs available to AI agents securely and in a controlled way, for example via the Model Context Protocol.

Course outline

Module 1 Understanding Bolt and WebContainers
  • Architecture: Node.js, npm and dev server running entirely in the browser
  • Supported frameworks and starter templates at a glance
  • Capabilities and limits of the browser runtime, for instance with native binary packages
  • Using editor, preview, terminal and file tree efficiently
Module 2 Prompting and project control
  • Writing a precise first prompt: stack, pages, data model, style
  • Project and system instructions for recurring conventions
  • Discussion mode for planning, build mode for implementing
  • Iterating in small steps, locking files and rolling back versions
Module 3 Full stack: data, auth and APIs
  • Database and authentication via the built-in backend integration or Supabase
  • Building API routes and server-side logic with Node.js
  • Connecting external APIs and managing keys through environment variables
  • Creating mobile prototypes with Expo and testing them on a device
Module 4 Debugging and code quality
  • Feeding terminal and browser console errors back to the agent effectively
  • Reading, understanding and manually correcting generated code
  • Keeping structure, component boundaries and TypeScript types clean
  • Managing token usage and context size as projects grow
Module 5 Deployment and handover
  • Publishing your app in one click and connecting a custom domain
  • Exporting to GitHub and continuing locally in VS Code, Cursor or Claude Code
  • Security review: secrets, access rights, input validation
  • Decision criteria: evolve the prototype or rebuild it properly
Module 6 Practice lab: API Development
  • Generating OpenAPI specs
  • Generating servers and clients
  • Building contract tests
  • Detecting breaking changes
  • Reviewing API security
  • Opening APIs to AI agents
Module 7 Data protection, quality & transfer to daily work
  • APIs frequently carry personal and confidential data: you learn to create specifications and sample data without real customer records, keep API keys and tokens out of the AI context and apply data protection by design under the GDPR as well as NIS2 requirements.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Generating OpenAPI specs

Draft complete OpenAPI specifications with schemas, examples, error objects and security schemes from requirements, domain models or existing code, and check them against your style guide rules.

Generating servers and clients

Derive controller scaffolding, validation logic and type-safe client SDKs for several languages from a specification, keeping generated parts cleanly separated from hand-written business logic. Regenerating after spec changes is simple.

Building contract tests

Define consumer-driven contracts, generate test cases for edge values, error paths and authentication, and run them automatically in CI against both the specification and the live implementation.

Detecting breaking changes

Compare two versions of a specification, identify incompatible changes, write migration notes for consumers and plan deprecation strategies using versioning and sunset headers. The check runs on every pull request.

Reviewing API security

Analyse endpoints for typical weaknesses from the OWASP API Security Top 10, such as broken object-level authorisation, mass assignment or missing rate limits, and propose fixes.

Opening APIs to AI agents

Expose existing interfaces as MCP servers or tool definitions, phrase descriptions and parameters so agents use them correctly and scope permissions so agents can only perform approved actions.

Possible hands-on projects

API linter with house rules

A command-line tool that checks OpenAPI files against your design guidelines and posts clear improvement suggestions directly on the pull request.

Mock server from spec

A small service that generates realistic sample responses from a specification so frontend teams can work in parallel with backend development.

MCP server for an internal API

An MCP server that exposes selected endpoints of an internal REST API as tools for AI assistants, with authentication and logging.

Prerequisites

Basic JavaScript or TypeScript skills and an understanding of common web concepts such as components, APIs and databases. Technically minded business users can follow along well too. You build backend applications in a mainstream language such as Java, C#, TypeScript, Python or Go and understand HTTP, JSON and REST fundamentals.

Technical requirements

A laptop with an up-to-date Chromium-based browser or Firefox, a Bolt account (a paid plan for intensive use) and a free GitHub account.

Data protection & compliance

APIs frequently carry personal and confidential data: you learn to create specifications and sample data without real customer records, keep API keys and tokens out of the AI context and apply data protection by design under the GDPR as well as NIS2 requirements.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Fri, 27 November 2026 Online Request
  • Fri, 4 December 2026 Kaiserslautern Request
  • Tue, 8 December 2026 Online Request
  • Fri, 8 January 2027 Augsburg Request
  • Fri, 15 January 2027 Online Request
  • Thu, 28 January 2027 Salzburg Request

Frequently asked questions

Do I need a local development environment for Bolt?

No. Thanks to WebContainers, package installation, dev server and terminal all run in the browser, so nothing needs to be installed locally. Only when you want to develop a project long term or integrate it into an existing toolchain does exporting to GitHub and working in a local editor pay off. We practise both in the course.

How is Bolt different from Lovable?

Both generate web apps from prompts. Bolt offers more freedom in choosing a framework and exposes code, terminal and file tree very directly, which suits developers who want to stay close to the code. Lovable relies on a fixed React stack with a stronger focus on design iteration and Supabase. The right choice depends on project and team.

Can I build mobile apps with Bolt?

Yes. Through the Expo integration you can generate React Native prototypes and test them on your phone with Expo Go. This works well for simple apps and clickable prototypes. Native device features, app store publishing and complex offline logic usually call for local development, and the course explains where that boundary lies.

Is spec-first still worth it when AI writes code so quickly?

More than ever. A precise specification is the best context you can give an AI assistant: it pins down data structures, error cases and security requirements, serves as a contract for consumers and underpins your tests. Without one, AI quickly produces inconsistent endpoints. In the course you use the spec as the single source for generation and validation.

What is the difference between a REST API and an MCP server?

A REST API is aimed at developers and applications, whereas an MCP server exposes capabilities in a standardised format for AI assistants and agents, including descriptions the model can interpret. An MCP server is often a thin layer on top of existing APIs. You learn when that step pays off and how to implement it securely.

How much does “Bolt for API Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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