Learning path
Next moduleModule 21: Safety and security
Module 20
Production AI engineering
Architecture, reliability, observability, cost, and latency for systems built around models.
- 1. Production architectureLay out the components of a production LLM application, from gateway to governance, and know each one's responsibility, failure modes, metrics, security concerns, and scaling limits.40 min
- 2. Reliability, cost, and observabilityMake AI services dependable with timeouts, retries, circuit breakers, and idempotency; cut cost and latency with caching, batching, KV caching, quantization, and distillation; and monitor drift in data, prompts, and quality.40 min
Role definition. An AI engineer combines model understanding with software engineering, data pipelines, evaluation, deployment, security, and product judgment.
| Layer | Responsibilities |
|---|---|
| Interface | API, UI, authentication, rate limits |
| Orchestration | prompt templates, routing, retries, structured outputs, tools |
| Knowledge | document ingestion, search, vector index, metadata filters |
| Model | hosted API or self-hosted inference, model versioning |
| Evaluation | offline test sets, online experiments, human review |
| Observability | traces, prompts, latency, token usage, errors, quality signals |
| Governance | privacy, access control, retention, audit, safety policies |
| # | Lesson | You will be able to |
|---|---|---|
| 1 | Production architecture | describe each component of an LLM application, its failures, and its metrics |
| 2 | Reliability, cost, and observability | apply timeouts, retries, circuit breakers, caching, and quantization, and monitor drift |