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Keep shoppingBuild AI Systems in Pure Go
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What you'll learn
- Build production-ready LLM clients in pure Go with structured outputs, error handling, token counting, and cost tracking.
- Transform events and database records into features and dynamic prompts that give LLMs relevant business context.
- Implement prompt chaining, routing, parallelization, and evaluator-optimizer workflows for complex decisions.
- Build a real-time notification decision engine using Go, NATS, PostgreSQL, and OpenAI.
- Make LLM workflows resilient with transactional outbox, event sourcing, circuit breakers, and provider fallbacks.
- Monitor LLM cost, token usage, latency, and response quality with Prometheus and Grafana.
- Scale production LLM systems with caching, horizontal scaling, database optimization, and multi-tenant architecture.
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Course content
4 sections • 12 lectures • 1h 11m total length
Requirements
- Intermediate Go experience: familiarity with REST APIs, JSON, goroutines, and basic database operations.
- Basic familiarity with backend systems and command-line development tools.
- For hands-on exercises, access to an OpenAI API key or another compatible LLM provider.
- No Python, machine learning, or data science experience is required.
Description
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.
Who this course is for:
- Intermediate Go developers who want to add LLM features without introducing Python services.
- Backend engineers building reliable, event-driven, or data-intensive AI systems.
- Tech leads and software architects evaluating production LLM architecture, reliability, observability, and cost controls.
- Go teams ready to move an LLM prototype into a scalable, production-ready system.