GuideIntermediate
Production Best Practices Guide
Master the engineering practices that separate AI prototypes from production systems: testing strategies for non-deterministic outputs, input/output guardrails, structured logging with cost tracking, clean architecture patterns for LLM applications, and reliability patterns including retry, circuit breakers, and fallback strategies.
- 64
- lessons
- 8
- modules
- English · Spanish
- available in
- Yes
- certificate
- Free
- access
Outcomes
What you'll be able to do
- Set up professional testing infrastructure for LLM applications with pytest fixtures and markers
- Write unit tests that mock LLM responses and validate prompts as behavioral contracts
- Handle non-deterministic outputs with semantic similarity assertions and property-based testing
- Implement guardrails pipelines with input sanitization, prompt injection defense, and output validation
- Detect and redact PII in LLM inputs and outputs
- Configure structured logging with correlation IDs, token tracking, and cost monitoring
- Apply clean architecture patterns specific to AI applications (prompt/logic/infra separation)
- Build reliability layers with retry, circuit breakers, rate limiting, and fallback strategies
Before you start
What you need to bring
It's for you if...
- AI Engineers who have built functional LLM applications and need to prepare them for production deployment
- Backend developers adding AI features who need professional testing and code quality standards
- Tech leads preparing teams to deploy AI systems with proper guardrails and observability
- Startups transitioning AI MVPs to production-ready products with reliability requirements
- Developers who want to differentiate themselves with professional AI engineering practices
Requirements and materials
- Python intermediate (OOP, decorators, type hints, Pydantic basics)
- Experience with REST APIs (FastAPI or similar)
- Basic LLM usage (OpenAI API or equivalent)
- Evaluation concepts from Guide #12 (metrics, golden datasets, quality measurement)
- Basic familiarity with pytest (running tests, simple assertions)
Content
The syllabus, module by module
Open any of them to see its lessons.
- 1. Module Introduction: Testing Fundamentals for AI
- 2. Why AI Needs Different Testing
- 3. pytest Configuration for AI Apps
- 4. Anatomy of the Test: Arrange-Act-Assert for LLM
- 5. Fixtures for LLM Apps
- 6. Taxonomy of Tests: Smoke, Contract, Behavioral, Regression
- 7. Project: Test Suite Setup
- 8. Troubleshooting and Module 1 Summary
Where it fits
This guide is part of something bigger
It's studied inside these programs, with support and dates.
Common questions
What people usually ask
No limit. It's a free guide: come in whenever you like, as often as you like.
No. Modules run from easier to harder, but you can jump to the one you need. Progress is saved per lesson.
Whatever is needed is listed under “What you need to bring”, above. If nothing is listed there, you can start from zero.
In the Club's WhatsApp group, and every two weeks there's a live with an instructor where questions get worked through.
Yes. It's issued automatically once you finish every lesson, with a verifiable code you can share on LinkedIn.
No. This guide is self-paced with no dates. The bootcamp is live, by cohort, with work someone reviews.
Start whenever you like
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