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AI & ML

Prompt Engineering

The practice of designing the input to a language model to get reliable, correct output, closer to interface design than to trickery.

Prompt engineering is the work of shaping what you send a model so that what you get back is reliable enough to build on: giving it the right context, the right constraints, examples of the format you want, and a clear definition of the task. Done well it is unglamorous and systematic, closer to writing a good specification than to finding magic words.

In a real system the prompt is not a one-off: it is code, versioned and tested like any other, and evaluated against a set of cases so a change that helps one input does not silently break ten others. When people say a model is unreliable, the fix is more often better prompting and evaluation than a better model.

Working out whether you need Prompt Engineering?

Definitions are the easy part. If you are trying to decide whether Prompt Engineering belongs in your system, describe what you are building and a senior engineer will give you a straight answer, including when the answer is that you do not need it.

  1. 01A senior engineer reads it. Not a form queue, and not an account manager.
  2. 02We reply either with questions or with a straight answer that we are not the right fit.
  3. 03If it looks like a fit, a technical call with the person who would actually run the delivery.
  4. 04Then scope, effort and risk in writing, before anyone signs anything.

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