Build AI Systems in Pure Go, Production LLM Course
Learn to build production AI systems in pure Go. Master LLM integration, event-driven workflows, and reliability patterns from Pinterest/Revolut engineer. No Python dependencies required.
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Watch the Course Overview
Don't Complicate Your Architecture for AI
Your Go system works. Adding Python AI services means more complexity, costs, and operational overhead.
The Hidden Cost of Python AI Services
Keep It Simple: AI in Your Existing Go System
LLMs are just HTTP APIs. OpenAI, Claude, Gemini - they're REST endpoints that return JSON. Add AI features without changing your architecture.
Business Impact: Lower Cost, Faster Delivery
Operational Savings:
- ✓ No new services to deploy/monitor
- ✓ Leverage existing Go team
- ✓ Single deployment pipeline
What You'll Build:
- 🤖 Intelligent notification systems
- ⚡ Event-driven AI workflows
- 📊 Production-ready decision engines
Real Production System You'll Build
An intelligent notification system that analyzes user behavior in real-time and decides when to send personalized notifications
Event-Driven AI Architecture
5-service architecture handling millions of events with AI-powered decision making
How It Works: Real-Time Intelligence
Event Processing
- ✓NATS streaming
- ✓Backpressure handling
- ✓Event sourcing
- ✓LLM trigger workflows
Feature Engineering
- ✓Real-time aggregations
- ✓Feature versioning
- ✓Caching strategies
- ✓LLM context preparation
AI Integration
- ✓Structured LLM outputs
- ✓Multi-provider support
- ✓Cost optimization
- ✓Fallback strategies
Production Ops
- ✓Prometheus metrics
- ✓Circuit breakers
- ✓Grafana dashboards
- ✓LLM cost tracking
7 Hands-On Production Modules
Build a complete AI system step-by-step, from LLM integration to production deployment
Go-Native LLM Integration
Build production LLM clients in pure Go
What You'll Master:
- ✓Pure Go client implementation
- ✓HTTP client configuration
- ✓Structured outputs for decisions
- ✓Token counting and cost tracking
- ✓Basic error handling patterns
Project Outcome:
High-performance LLM service in Go
Feature Engineering for LLM Prompts
Extract features from events and databases to create intelligent LLM prompts
What You'll Master:
- ✓Feature extraction from events and databases
- ✓Feature aggregation and transformation
- ✓Dynamic prompt template construction
- ✓Feature injection into LLM context
- ✓Prompt optimization with feature data
Project Outcome:
Feature-driven prompt generation system
LLM Patterns & Workflows
Master advanced LLM patterns for complex decision making
What You'll Master:
- ✓The augmented LLM pattern
- ✓Workflow: Prompt chaining
- ✓Workflow: Routing decisions
- ✓Workflow: Parallelization strategies
- ✓Workflow: Evaluator-optimizer loops
Project Outcome:
Multi-pattern LLM orchestrator
Production LLM Decision Engine
Build reliable LLM workflows for business-critical decisions
What You'll Master:
- ✓Prompt quality evaluation techniques
- ✓Model selection for production use
- ✓LLM workflow testing strategies
- ✓Notification decision system implementation
- ✓Quality assurance and reliability patterns
Project Outcome:
Production-ready notification decision engine
Production Reliability Patterns
Build fault-tolerant LLM systems that handle failures gracefully
What You'll Master:
- ✓Transactional outbox pattern
- ✓Event sourcing for AI calls
- ✓Circuit breakers for providers
- ✓Fallback strategies
- ✓Cost controls
Project Outcome:
Bulletproof delivery system
Monitoring & Optimization
Monitor LLM performance, costs, and quality with production-grade observability
What You'll Master:
- ✓Prometheus metrics for LLM systems
- ✓Grafana dashboards for AI workflows
- ✓Cost monitoring and budget controls
- ✓Token usage and latency tracking
- ✓LLM response quality monitoring
Project Outcome:
Complete LLM observability stack
Scaling to Production
Scale LLM systems to handle millions of events with optimal performance and cost efficiency
What You'll Master:
- ✓Horizontal scaling patterns for LLM workflows
- ✓Database optimizations for feature queries
- ✓LLM response caching strategies
- ✓Multi-tenant LLM system architecture
- ✓Production deployment and cost optimization
Project Outcome:
Enterprise-scale LLM notification system
What You'll Be Able to Do
Learn from a Production Systems Expert
Built scalable systems at Pinterest, Revolut, and created Go-native AI tools in production
Vitalii Honchar
Senior Software Engineer
I've built production systems that handle millions of requests at scale. After years of infrastructure work, I saw how AI could enhance these systems—but only if done right. I created this course to teach the infrastructure-first approach to AI that actually works in production.
Production Infrastructure
- Pinterest: Continuous deployment platform
- Revolut: Feature Store at banking scale
- Form3: Financial processing systems
AI Systems Innovation
- Building Go-native LLM orchestration tools
- Published AI + infrastructure patterns
- Consulting on production AI systems
💡 "I teach the patterns that actually work when your AI system needs to handle real traffic, real failures, and real business requirements."
Production-Grade Technology Stack
Master the tools used by top tech companies for scalable AI systems
Real production stack used at Pinterest, Revolut, and other scale-ups
Common Questions About This Course
Everything you need to know before building production LLM systems in Go
Do I need Python or ML background?
Not at all! This course is 100% Go. I treat LLMs as HTTP APIs—if you can build REST services in Go, you can build AI systems. No ML theory or Python required.
Will this work with my existing Go architecture?
Yes! The patterns work whether you have a monolith, microservices, or event-driven architecture. I show how to add LLM features without changing your core system.
Is this production-ready or just tutorial code?
Production-ready. You'll build a complete notification system with circuit breakers, monitoring, cost controls, and reliability patterns used at companies like Pinterest and Revolut.
What about other LLM providers besides OpenAI?
The patterns work with any HTTP-based LLM API (Claude, Gemini, etc.). I start with OpenAI for consistency, then show multi-provider patterns.
How is this different from Python AI frameworks?
Python frameworks add complexity for Go teams. This course teaches infrastructure-first patterns that leverage your existing Go expertise—no new languages or frameworks.
What level Go experience do I need?
Intermediate. You should be comfortable with HTTP servers, JSON handling, goroutines, and basic database operations. If you've built APIs in Go, you're ready.
Still have questions?
Email me at vitaliy.gonchar.work@gmail.com and I'll help you determine if this course is right for your team and project.
Ready to Build AI Systems in Pure Go?
Stop polluting your Go projects with Python. Start building production AI systems that scale.
30-day guarantee • Lifetime updates • Real production system