What makes a production prompt more than a string?

Treats a production prompt as a versioned request contract containing instructions, context, output expectations, and application-owned controls.

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2CompareCompare with Core answer

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TL;DR

A production prompt is the complete set of instructions and context an application sends to a model for a defined task. It normally includes the task, relevant input, constraints, and the expected response shape. I treat it as application configuration rather than scattered string concatenation, because a wording change can change behavior just like a code or dependency change.

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Practice focus

Whether the candidate can move beyond ad hoc prompt text and describe the prompt as one tested part of an application boundary.

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Answer path

Core answer first, then checkpoints toward a stronger answer.

Start
Core answer
Start here. This is the direct spoken answer to practice first.
Overview
The useful distinction is not between a clever and an ordinary sentence. It is between an ad hoc string and an explicit, testable request contract.
A production prompt is the complete set of instructions and context an application sends to a model for a defined task. It normally includes the task, relevant input, constraints, and the expected response shape. I treat it as application configuration rather than scattered string concatenation, because a wording change can change behavior just like a code or dependency change.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
For a support-ticket classifier, I would separate the stable task instructions from the ticket text, define the allowed categories and what to do when evidence is insufficient, and request a typed result. The application still validates that result before using it. That makes the boundary understandable and testable; adding more persuasive prose without a clear contract usually makes the prompt harder to reason about.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Describing prompt engineering as finding magic wording.
Treating only the final user sentence as the prompt.
Embedding business rules in prose without application validation.
Changing prompts in production without versioning or evaluation.

What makes a production prompt more than a string?

Practice focus

Answer path

Overview

Review signals

Follow-up prompts

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