Production AI Workflows

Introduction

A production AI workflow is more than the LLM call — it includes ingestion, evals, observability, deploys, rollbacks, and feedback loops. Treat it like any critical system.

Beginner analogy: Like CI/CD for AI — test, deploy, monitor, roll back when needed.

Understanding the topic

Core concepts to understand:

• Prompt & model versioning in git.
• Evals run on every PR.
• Canary new models to 5% of traffic first.
• Rollback button always one click away.
• Feedback loop → datasets → next eval / fine-tune.

Syntax reference

Visual workflow / architecture:

PR ─► Eval suite ─► Canary 5% ─► 100% rollout
                                  │
                                  ▼
                         monitoring + feedback
                                  │
                                  ▼
                          new dataset → re-eval

Real-world use

OpenAI, Anthropic, Cursor, Perplexity all run rigorous AI CI/CD pipelines.

Best practices

• Version everything (prompt, model, eval set).
• Always canary new models.
• Make rollback trivial.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What does AI CI/CD look like?
• Q2. Why canary new models?
• Q3. How do feedback loops drive improvement?