Phase 8: Production Hardening & Operations · 50 min · Python · Redis · Kubernetes

Reliability Engineering for AI

Retries, fallbacks, circuit breakers. The AI system stays up.

Hiring signal: FDE interviews test reliability patterns: candidates who implement retries with exponential backoff and jitter, model fallbacks (GPT-4o → Claude → Llama), and circuit breakers for cascading failure prevention pass. Candidates who have no fallback strategy fail. Graceful degradation (partial responses, reduced functionality modes) is the production pattern that keeps AI systems running when upstream providers have outages.

What you will learn

Implement retry patterns: exponential backoff, jitter, max retries, retryable vs. non-retryable errors
Configure fallbacks: model fallback (GPT-4o → Claude → Llama), retrieval fallback (vector → keyword → cached)
Build circuit breakers: detecting cascading failures, opening/closing circuits, half-open recovery
Implement graceful degradation: partial responses, reduced functionality modes, maintenance mode
Configure rate limiting: per-user, per-tenant, global, token bucket vs. sliding window, 429 handling

What You'll Learn

This lesson takes approximately 50 min. By the end, you will be able to:

Implement retry patterns: exponential backoff, jitter, max retries, retryable vs. non-retryable errors
Configure fallbacks: model fallback (GPT-4o → Claude → Llama), retrieval fallback (vector → keyword → cached)
Build circuit breakers: detecting cascading failures, opening/closing circuits, half-open recovery
Implement graceful degradation: partial responses, reduced functionality modes, maintenance mode
Configure rate limiting: per-user, per-tenant, global, token bucket vs. sliding window, 429 handling

The Problem

AI systems fail in ways traditional software doesn't: model providers rate-limit you, inference times spike unpredictably, and LLM non-determinism means the same input can produce different outputs. Reliability engineering for AI means building systems that stay up: retries with exponential backoff, model fallbacks, circuit breakers, and rate limiting.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Retry Patterns: Exponential Backoff with Jitter, Model Fallback Chain, Circuit Breaker for LLM APIs, Rate Limiting: Token Bucket, Practical Application, What Hiring Managers Look For, Key Takeaways, Next Steps — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Token