One prompt structure decision decides whether your AI agent stays contained or leaks everything it touches.

There are five of these decisions that matter most in a real production system. Most teams get one or two right and never notice the other three are missing, until something breaks in front of a client.

Here is what that actually looks like across a real stack.

Separating instructions from data is a security boundary, not a formatting choice. Wrap outside content (a document, a retrieved chunk, a tool output) in a clear structural tag. Tell the model explicitly: this is text to read, not a command to follow. This is the same principle already used to stop SQL injection in a normal database query.

Forcing step by step reasoning before an answer is also a hallucination control. Ask a model to list edge cases before it commits to a conclusion. Failure rate drops, because it cannot skip past its own stated reasoning. This is the same job your eval gate does before a release.

Few-shot examples are a schema contract. One filled-in example, and the model stops improvising the output format. Your observability stack only works if every run outputs the same shape. Nobody can trust a dashboard built on data that changes shape every run.

Explicit permission to say "not sure" is a compliance artifact. A model that states uncertainty gives you something real to review later. Confident wrong answers are unauditable. Labeled uncertainty belongs in the evidence file.

Breaking a large task into dependent steps mirrors a LangGraph node graph. One prompt asking for everything fails the same way a monolithic function fails. Decompose it. Review each output before the next step runs. Errors get caught early instead of compounding silently through the chain.

This is the same discipline secure system design already runs on, applied one layer earlier, at the prompt itself.

Five decisions. Most production systems right now are missing at least two of them, and nobody notices until the wrong input arrives at the wrong time.

Which of these five do you see teams skip most often once a prototype turns into a production system?

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