Production Readiness Runbook for LLM Systems
Every LLM system looks great in the playground. Then it hits real users, real scale, and a vendor model update it wasn’t expecting - and the gap between demo and production becomes expensive. This course is the operational discipline that closes that gap: a practitioner’s runbook covering monitoring, failure taxonomies, incident response, deployment gates, and rollback strategies for LLM systems that actually have to work.
Course Lessons
From the production gap to the full readiness playbook - follow in order or jump to the topic your system needs most.
1. The Production Gap
Why LLM systems that work in the playground fail at scale - the four failure vectors, what production-ready really means, and the cost of incidents.
2. The Pre-Deploy Checklist
A comprehensive 40+ point checklist covering infrastructure, prompts, output validation, red-teaming, monitoring, security, and rollback plans.
3. Monitoring Patterns
The 5 signals to always monitor, SLOs appropriate for LLM systems, the gateway as observability hub, and how to detect silent quality degradation.
4. LLM Failure Modes
A taxonomy of 6 production failure categories: hallucination at scale, prompt injection, context overflow, rate limits, latency spikes, and silent quality drift.
5. Incident Response
Why LLM incidents need special playbooks, severity tier definitions, the full response playbook, and the post-mortem template built for LLM systems.
6. Rollback & Safe Deployment
Why LLM rollback is harder than code rollback - and the 4 deployment patterns that give you a safe way back: blue/green, canary, shadow mode, and feature flags.
7. Deployment Gates
The 5 gates every LLM release must pass, the go/no-go decision matrix, the approval workflow, and CI pipeline integration for the regression gate.
8. The Production Readiness Playbook
Everything compressed: the LLM maturity model, the 30-day hardening plan, the 10 non-negotiables, and the is-this-system-ready decision tree.
What You Will Learn
By the end of this course, you will be able to:
Ship With Confidence
Use the 40-point pre-deploy checklist and deployment gates to catch every class of LLM failure before it reaches users.
Detect Failures Early
Build the monitoring stack - latency, quality score, hallucination rate, user satisfaction proxy - that catches silent degradation before users do.
Respond Fast
Apply the LLM-specific incident response playbook so your team knows exactly what to do in the first 10 minutes of any production incident.
Never Lose a Deploy
Implement blue/green, canary, shadow mode, and feature-flag patterns so every LLM deployment has a safe, tested path back.
Go Deeper: Companion Courses
This runbook covers operations. These courses cover the engineering that makes your system worth operating.
Prompt Patterns That Survive Production
Output contracts, failure-mode diagnosis, and the 25-point pre-deploy checklist that pairs with this course’s operational discipline.
Token Optimization
Cut your AI costs 50-90% without cutting capability - caching, routing, context engineering, and governance for organizations.
AI Agent Frameworks in Practice
LangGraph, CrewAI, and OpenAI Agents SDK compared - with the production lessons applied to real framework code.
Prompt Engineering
The fundamentals that underpin every production prompt: chain-of-thought, few-shot, structured output, and beyond.
Running a Website with a Fleet of AI Agents
Production operations in practice: how a live fleet uses the monitoring, guardrail, and recovery patterns from this runbook - with real failure stories and a build-your-own playbook.
Forward Deployed AI Engineer
The role that carries this runbook into customer environments - how FDAEs use the pre-deploy checklist, monitoring setup, and knowledge-transfer handoff in real customer engagements.
AI Hallucination
The production-quality layer below this runbook: detecting, preventing, and monitoring hallucination before and after deployment.
Model Tuning
Know when to tune vs. prompt vs. RAG, then build the full pipeline: data preparation, LoRA/QLoRA, evaluation, and the tuning playbook your deployed models need.
Model Drift
What to do when production performance degrades over time: statistical drift detection, LLM-specific monitoring, root cause analysis, and the retrain vs. rollback decision framework.
Go Deeper With Expert Courses
Recommended learning resources from our partners. Affiliate disclosure.
DataCamp - AI & Data Science
Hands-on Python, machine learning, and AI courses with interactive exercises and real projects. Track-based learning for practitioners.
DataCampedX - Top AI Courses
Courses and MicroMasters from MIT, Harvard, Stanford, and other top universities. Earn certificates that employers recognize.
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