Tutorials
Tutorials
End-to-end AI observability tutorials - RAG tracing, production monitoring, cost optimization, and agent evaluation
Tutorials
Comprehensive, step-by-step tutorials for implementing AI observability in real-world scenarios.
Available Tutorials
RAG Application Tracing
Add full observability to retrieval-augmented generation pipelines
Agent Evaluation
Evaluate AI agents with tool use and multi-step reasoning
Cost Optimization
Reduce AI costs while maintaining quality
Production Monitoring
Set up monitoring, alerts, and dashboards for production AI
Tutorial Structure
Each tutorial follows a consistent format:
- Prerequisites: What you need before starting
- Overview: What you'll build and learn
- Step-by-step Implementation: Detailed instructions with code
- Testing & Validation: How to verify your implementation
- Next Steps: Where to go from here
Quick Reference
| Tutorial | Time | Difficulty | Key Concepts |
|---|---|---|---|
| RAG Tracing | 30 min | Intermediate | Spans, retrieval, generations |
| Agent Evaluation | 45 min | Advanced | Tool use, multi-step, LLM judge |
| Cost Optimization | 30 min | Intermediate | Cost tracking, model selection |
| Production Monitoring | 45 min | Advanced | Alerts, dashboards, SLOs |
Prerequisites
All tutorials assume you have:
- A Brokle account with an API key
- Python 3.9+ or Node.js 18+
- Basic familiarity with LLM APIs (OpenAI, Anthropic)
Getting Help
If you encounter issues:
- Check the Troubleshooting guide
- Review the API Reference
- Join our Discord community