If you’re putting LLMs anywhere near production ops, you need guardrails. In this episode of DevOps Unpacked we break down how AI fails in real systems (hallucinations, prompt injection, and data leakage) and what to do about it.

We cover practical defences: treating prompts and retrieved text as untrusted input, using RAG safely (scoping and access control), least-privilege tool access for agents, keeping secrets out of prompts, and adding auditability + evals so your controls don’t rot over time.

AI can write code. That doesn’t mean it can run production.

In Episode 3, we break down what AI actually changes in DevOps and software engineering — and what stays stubbornly human: requirements, judgement, risk, guardrails, and the messy reality of pipelines and production systems.

We cover:

• Why “AI will replace engineers” is the wrong question

• Where AI genuinely saves time in day-to-day DevOps work

• Why requirements and review matter more (not less) with AI

• CI/CD, evidence, and the controls AI can’t shortcut

• The junior vs senior impact: how teams and careers shift

Chapters:
00:00 AI won’t kill software engineering
03:30 AI as a force multiplier
07:00 Tools vs real understanding
10:30 Requirements still matter
14:00 Pipelines and real-world constraints
18:00 Frameworks and guardrails
22:30 Safe AI in production
27:30 Where AI actually saves time
32:30 AI in daily workflows
37:00 Skills that matter more with AI
41:30 Junior vs senior impact
46:00 Long-term team impact
50:30 Engineers aren’t going anywhere

If you work in DevOps, Platform Engineering, SRE, or you’re building software that has to pass audits and survive incidents — this one’s for you.

In this episode of DevOps Unpacked, we tackle the crucial differences between CI CD, continuous integration, and continuous delivery. 🤔 Join us as we explore how tools like Jenkins and GitHub Actions enhance the CI CD pipeline, ensuring smooth deployments. Don't miss out on this essential guide to mastering devops concepts! 🚀