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
Included in the Club
access
NIEVA

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

  • 1. Introduction: Unit Testing LLM Applications
  • 2. Mocking LLM Responses
  • 3. Prompt Contract Tests
  • 4. Snapshot Testing
  • 5. Testing Parsers and Output Processors
  • 6. Fixture Factories
  • 7. Project: Prompt Contract Tests
  • 8. Module 2 Summary and Troubleshooting

  • 1. Introduction: Integration Testing for AI
  • 2. End-to-End Tests
  • 3. Real LLM vs Mocks: Decision Framework
  • 4. Semantic Similarity Assertions
  • 5. Property-Based Testing
  • 6. Flaky Test Management
  • 7. Project: Integration Test Suite
  • 8. Module 3 Summary and Troubleshooting

  • 1. Introduction to Guardrails
  • 2. Input Sanitization
  • 3. Prompt Injection Defense
  • 4. Output Validation with Pydantic
  • 5. Content Filtering
  • 6. PII Detection and Redaction
  • 7. Project: Guardrails Pipeline
  • 8. Summary and Troubleshooting for Module 4

  • 1. Introduction: Structured Logging for AI
  • 2. Logging Strategies for LLM
  • 3. Request Tracing with Correlation IDs
  • 4. Token and Cost Tracking
  • 5. JSON Logs with structlog
  • 6. Debugging Non-Deterministic Systems
  • 7. Project: AI Logging System
  • 8. Module 5 Summary and Troubleshooting

  • 1. Introduction: Code Quality for AI
  • 2. Clean Architecture for AI
  • 3. Separation of Concerns
  • 4. Config Management
  • 5. Dependency Injection for LLM Providers
  • 6. Environment Management
  • 7. Project: Refactored AI App
  • 8. Module 6 Summary and Troubleshooting

  • 1. Introduction: Reliability Patterns
  • 2. Error Handling for LLM APIs
  • 3. Retry with Exponential Backoff
  • 4. Circuit Breakers
  • 5. Rate Limiting
  • 6. Fallbacks and Health Checks
  • 7. Project: Reliability Layer
  • 8. Summary and Troubleshooting for Module 7

  • 1. Production Checklist
  • 2. Deployment Strategies
  • 3. Pre-Launch Validation
  • 4. Integrating Testing, Guardrails, Logging, and Reliability
  • 5. Project: Production AI System
  • 6. Performance Baselines
  • 7. Operational Runbook
  • 8. Summary and Guide Wrap-Up

Where it fits

This guide is part of something bigger

It's studied inside these programs, with support and dates.

AI Engineering Bootcamp [January 2026]AI Engineering Bootcamp [Agosto 2026]

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