Stop Trusting LLM Output Blindly
Not long ago, every AI tool carried a small disclaimer at the bottom of the screen: always verify AI-generated content. We read it, nodded, and moved on. Somewhere along the way, we stopped reading it altogether. That is a problem.
Accountability Is the Defining Skill of the AI Era
The single most important thing to understand about working with large language models is this: you are responsible for everything they produce on your behalf. The model is not. The platform is not. You are.
This is especially true in software development. As a developer, reviewing every line of AI-generated code is not optional — it is the job. That means reading it, understanding it, verifying there are no bugs or security vulnerabilities, and updating your own mental model of the codebase accordingly. LLMs are remarkably capable at writing syntactically plausible code. They are far less reliable when it comes to knowing your team's conventions, your project's standards, or the broader architecture of a large, complex codebase. That gap is yours to close.
The same principle extends beyond code. If an LLM drafts an email on your behalf and you send it without reading it, you own whatever it says. If it misrepresents something, offends a client, or loses you a relationship, that outcome is yours. The model will not be held accountable. You will.
Stop Granting Excessive Permissions by Default
Another pattern worth correcting: giving LLMs and AI-powered tools broad access to your systems and data without thinking carefully about what they actually need.
The principle here is simple. Grant the minimum permissions necessary, nothing more. If you create API tokens or credentials for an AI tool, scope them tightly, rotate them regularly, and test them thoroughly before putting them into any workflow that matters. Treating AI tools as trustworthy by default, and giving them write access to production systems or sensitive data, is a risk that compounds quickly and quietly.
A Closing Note on Automating Too Much
Someone recently mentioned to me that they had automated their email responses entirely with an LLM, spending significant effort making the replies sound personal, specifically so recipients would not realize the responses were machine-generated. Leaving aside the ethical dimension of that choice, the practical risk is significant. If you are not reading the emails coming in, and not reviewing the replies going out, you are eventually going to send something you should not have. It is not a question of if.
And if everyone automates both the sending and receiving of email, we will have arrived at a situation where no one is actually communicating with anyone, just two language models exchanging pleasantly worded text on behalf of people who stopped paying attention.
Review the output of your LLMs.