Training course · Vibe Coding · Go

Vibe Coding for Go Developers

Build software in natural language: from prompt to app, rapid iteration, quality and security checks, and turning prototypes into production.

Duration
2 days (14 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 Vibe Coding with the everyday processes of Go. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Vibe coding is a new style of software development: you describe in natural language what an application should do, and AI tools generate the code for it. Instead of writing every line yourself, you steer through descriptions, examples and feedback, test the result and refine it in short loops. This dramatically speeds up prototypes, internal tools and experiments. This vendor-neutral course shows you how to use vibe coding productively with tools such as Claude Code, Cursor, Copilot or browser-based app builders.

Knowing the limits matters just as much as speed. Uncontrolled generated code can be unmaintainable, insecure or simply wrong. You will therefore learn to state requirements precisely, safeguard results with tests and reviews, spot typical security flaws and steer the architecture deliberately. Finally, you will map out a clear path from quick prototype to production-ready application, covering version control, deployment, documentation and a clean handover to the team that will run and maintain the solution over the long term.

Go is the language behind many cloud-native tools, microservices, CLIs and infrastructure components. Its strengths are simplicity, fast compilation and built-in concurrency – yet goroutines, channels, context handling and error handling all demand discipline. AI coding assistants and agents suit Go particularly well: the compiler, go vet and the race detector provide fast, unambiguous feedback that agents can iterate on. Typical tasks include table-driven tests, gRPC and HTTP handlers generated from specifications, tracking down race conditions or goroutine leaks and refactoring towards clean interfaces. This course shows you how to get idiomatic Go from AI and how to verify the results with Go’s own tooling.

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?

  • Go developers in back-end and platform teams
  • Developers of cloud-native tools, operators and CLIs
  • Site reliability engineers maintaining their own Go code
  • Software architects designing high-performance microservices

Typical challenges we address

  • Subtle concurrency bugs such as race conditions, deadlocks and goroutine leaks
  • Repetitive boilerplate in error handling, handlers and tests
  • Inconsistent project layouts and interface boundaries in growing codebases
  • Difficult performance analysis of memory allocations and latency

What you will be able to do

  • You build working applications in natural language with a suitable AI tool.
  • You phrase requirements and constraints so that generated results meet your expectations.
  • You iterate purposefully, fix errors efficiently and stay in control of the project state.
  • You safeguard generated code with tests, reviews and security checks.
  • Have AI agents produce idiomatic Go code, tests and services that follow Go conventions.
  • Use AI deliberately to analyse concurrency and performance problems.
  • Verify generated code with the compiler, go vet, the race detector, linting and benchmarks.

Course outline

Day 1

Module 1 Vibe coding fundamentals
  • What vibe coding is and how it differs from traditional and agentic development
  • The tool landscape: AI editors, coding agents and browser-based app builders
  • Good use cases: prototypes, internal tools, experiments, learning projects
  • Roles: you as product owner, the AI as the one doing the typing
Module 2 From prompt to app
  • Describing requirements as a short spec covering users, features and boundaries
  • Choosing the tech stack and project structure deliberately rather than leaving it to the AI
  • Generating user interfaces from descriptions, sketches and reference examples
  • Adding data, APIs and persistence step by step
Module 3 Iterating and fixing problems
  • Working in small, verifiable steps and committing regularly
  • Feeding error messages, logs and screenshots back to the AI
  • Recognising stuck sessions, rolling back and restarting with fresh context
  • Creating project rules and context files to avoid repeating yourself
Module 4 Securing quality and safety
  • Having the AI generate tests and critically assessing what they actually prove
  • Reviewing generated changes for readability, duplication and needless complexity
  • Spotting typical security flaws: secrets in code, missing validation, exposed endpoints
  • Assessing dependencies, licences and data protection in generated solutions

Day 2

Module 5 From prototype to production
  • Setting criteria for evolving a prototype versus rebuilding it
  • Refactoring, cleaning up architecture and documenting with AI support
  • Setting up version control, CI and deployment for generated applications
  • Planning operations, monitoring and handover to a team or IT
Module 6 Practice lab: Go
  • Writing table-driven tests
  • Implementing gRPC and HTTP services
  • Finding concurrency bugs
  • Idiomatic refactoring
  • Profiling performance
  • Building CLIs and tools
Module 7 Data protection, quality & transfer to daily work
  • Go tooling often runs deep in the infrastructure, so keep kubeconfigs, tokens and certificates strictly out of an AI assistant’s context. Check new modules for provenance, licence and known vulnerabilities, for instance with govulncheck, and record dependencies in an SBOM as required under the Cyber Resilience Act for products with digital elements.
  • 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

Writing table-driven tests

The agent generates idiomatic table-driven tests with subtests, test helpers and fakes for interfaces, runs go test with the race detector and adds missing edge cases until coverage is where you want it.

Implementing gRPC and HTTP services

Protobuf definitions or OpenAPI specs become handlers, middleware for logging and authentication, context propagation with timeouts and clean error returns. Integration tests exercise the endpoints against realistic requests and failure cases.

Finding concurrency bugs

The AI analyses race detector output, goroutine dumps and code using channels or mutexes, explains the cause of deadlocks or leaks and proposes fixes using errgroup or context cancellation.

Idiomatic refactoring

Large packages are untangled, small consumer-side interfaces introduced, errors wrapped and checked with errors.Is, and code aligned with golangci-lint rules. Existing tests keep behaviour safe throughout.

Profiling performance

pprof profiles and benchmark results are interpreted, hotspots and unnecessary allocations identified, and optimisations backed by before-and-after benchmarks. Improvements become measurable rather than merely assumed.

Building CLIs and tools

The assistant creates command-line tools with Cobra or the standard library, including configuration, structured logging, cross-compilation and release automation. Tests, help texts and clear command documentation are included.

Possible hands-on projects

Health check aggregator

A Go service that polls the health endpoints of many services concurrently, merges the results and exposes them as JSON and Prometheus metrics.

Log shipper CLI

A lean command-line tool that tails log files, filters entries and ships them in batches to an HTTP endpoint – with backoff and graceful shutdown.

Kubernetes resource report

A CLI built on client-go that reads resource requests and limits across all deployments and presents anomalies in a clear table.

Prerequisites

Basic programming knowledge and some Git experience are helpful; you should be able to read simple code and make sense of error messages. Hands-on Go experience including goroutines, interfaces and modules, plus a working knowledge of Git and containers.

Technical requirements

Your own laptop with an up-to-date browser, Git and an AI coding tool of your choice, such as an AI editor or coding agent with an active account.

Data protection & compliance

Go tooling often runs deep in the infrastructure, so keep kubeconfigs, tokens and certificates strictly out of an AI assistant’s context. Check new modules for provenance, licence and known vulnerabilities, for instance with govulncheck, and record dependencies in an SBOM as required under the Cyber Resilience Act for products with digital elements.

Suggested dates

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

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Berlin Request
  • Tue, 15 December 2026 – Wed, 16 December 2026 Online Request
  • Mon, 18 January 2027 – Tue, 19 January 2027 Stuttgart Request
  • Mon, 1 February 2027 – Tue, 2 February 2027 Online Request
  • Tue, 16 February 2027 – Wed, 17 February 2027 Hanover Request

Frequently asked questions

Is vibe coding only suitable for quick prototypes?

Vibe coding shines with prototypes, internal tools and experiments. With the right practices, namely clear specifications, tests, reviews and sound version control, results can also be turned into production-ready applications. The course teaches you to manage that transition deliberately and to recognise early on when rebuilding is the wiser option.

Which tools are used in the course?

The course is vendor-neutral. We work with the main tool categories: AI editors such as Cursor or Windsurf, coding agents such as Claude Code or Codex, and browser-based app builders. You are welcome to use your preferred tool, as the principles of specification, iteration, quality and security apply to all of them.

How should I deal with security risks in generated code?

Generated code often contains typical weaknesses such as hard-coded credentials, missing input validation or overly open interfaces. You learn to check specifically for these patterns, build security requirements into the specification from the start, use automated scanning and require a deliberate human sign-off before every deployment.

Does AI really write idiomatic Go?

With good context, yes. Without guidance, models sometimes drift towards patterns from other languages, such as large interfaces or excessive abstraction. On the course you capture your Go conventions, linter configuration and example code so the assistant favours simple, idiomatic solutions, and you verify the output with golangci-lint.

Can AI reliably detect race conditions?

AI is a powerful analysis aid but no substitute for the race detector. The combination works best: the agent runs tests with the race detector, interprets the reports and proposes fixes. You learn to set up that loop and to critically assess any suggested synchronisation changes before accepting them.

How much does “Vibe Coding for Go Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 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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