Build AI Systems in Pure Go

Most LLM tutorials stop after sending a prompt to an API and printing the response. Real production systems need much more: reliable workflows, structured outputs, business context, failure handling, observability, cost controls, and scalable architecture.

This course teaches you how to build production-ready AI systems entirely in Go—without introducing Python services or heavyweight AI frameworks.

Throughout the course, you will build an intelligent notification system that consumes user events, prepares relevant features, constructs dynamic prompts, uses an LLM to make structured decisions, and delivers personalized notifications reliably.

You will learn how to:

• Integrate LLM APIs using idiomatic Go, HTTP clients, structured JSON outputs, error handling, and token tracking
• Extract and transform data from events and databases into useful context for LLM prompts
• Implement prompt chaining, routing, parallelization, and evaluator-optimizer workflows
• Build a reliable LLM decision engine for business-critical use cases
• Apply transactional outbox, event sourcing, circuit breakers, retries, and provider fallbacks
• Track token usage, latency, costs, failures, and response quality
• Scale LLM workloads using caching, database optimization, horizontal scaling, and multi-tenant architecture

The project uses a practical production stack including Go, PostgreSQL, and OpenAI. Although OpenAI is used for the primary examples, the architectural patterns can also be applied to other HTTP-based LLM providers such as Claude and Gemini.

This is an intermediate course for Go developers, backend engineers, technical leads, and software architects who want to add AI capabilities to existing systems without creating a separate Python stack.

You should be comfortable building REST APIs in Go, working with JSON, using goroutines, and performing basic database operations. No Python, machine-learning, or data-science experience is required.

By the end of the course, you will understand not only how to call an LLM, but how to design, operate, monitor, and scale an AI-powered backend system under real production constraints.