Think Build Implement Repeat
London, UK +44 7367 067226
WhatsApp FOLLOW f in X
  1. Home
  2. Blog
  3. Prompts Are Code: Managing Them Properly
AI & Machine Learning

Prompts Are Code: Managing Them Properly

Managing production prompts like code: keep them in one place, version them, run an evaluation set before changes and log which version made each output.

Updated 2 min readBy SpiderHunts Technologies

Free estimateNo obligation

Get a free estimate

Tell us what you need. A senior engineer reads every enquiry.

Takes under a minute. We never share your details.

  • Free consultation
  • No commitment
  • NDA on request

Prefer to talk? Book a free 30-minute call →

Quick answer — TL;DR

Treat prompts as versioned configuration with tests, not as strings buried in code. Keep them in one place, version them, run an evaluation set before any change ships, and log which version produced which output.

The problem appears around month three

Early on, prompts live wherever they were written. Then a model updates, output quality shifts, somebody tweaks a prompt to fix it, and something else breaks that nobody noticed for a fortnight.

By then there are prompts in six files, no record of what changed, and no way to tell whether last week's edit helped or harmed.

Four practices that prevent it

  1. One place. All prompts in a single location, not scattered through application code.
  2. Versioned. In source control, with a version identifier recorded on every output.
  3. Tested. An evaluation set that runs before any prompt change ships.
  4. Reviewed. Prompt changes go through the same review as code changes, because they have the same blast radius.

Log the version with the output

When someone disputes an answer the system gave three weeks ago, you need to know which prompt and which model produced it. Without that, investigation is guesswork.

Store prompt version, model identifier, retrieved context references and the output. Storage is cheap and this record is what makes debugging possible.

Separate the parts that change

A prompt usually has three components: stable instructions, retrieved or supplied context, and the user's input. Keep them structurally separate.

This also matters for cost: putting the stable part first makes prompt caching effective, which can be a substantial saving at volume.

Change one thing at a time

Prompt changes interact. Adjusting instructions and swapping models in the same release means you cannot attribute the difference in behaviour.

Ship one variable at a time, run the evaluation set, record the result. Slower, and it is the difference between improving deliberately and changing things hopefully.

Who is allowed to edit them

There is a temptation to let non-technical staff edit prompts directly, since they are just text. That works only with the same safeguards code has: versioning, testing and review.

A reasonable arrangement is that anyone can propose a change and the evaluation set decides whether it ships.

FAQ

Frequently asked questions

The questions readers ask us after this guide.

Still have a question?

Ask us directly — a senior engineer will get back to you.

Ask about your project

Do we need a prompt management platform?

For a handful of prompts, source control and a test script are enough. Platforms earn their place with many prompts, several teams, or a need for non-technical editing with guardrails.

How big should the evaluation set be?

A few hundred real cases with agreed correct answers covers most needs. Quality and representativeness matter far more than size.

What happens when the provider updates the model?

Behaviour can shift, sometimes subtly. Pin versions where the provider allows it, and re-run the evaluation set when you move to a new one.

Should prompts be treated as intellectual property?

They have some value and less than people assume — the value is usually in the evaluation set and the surrounding system. Keep them in your repository like everything else.

Keep reading

More on AI & Machine Learning

Start here

Prompts scattered through your codebase?

It becomes a problem around the third model update. Happy to review how yours are managed and what to tighten.

  1. You tell us what you needTwo minutes on the form, or a message on WhatsApp.
  2. A senior engineer reviews itAnd comes back with questions, a realistic range and an honest view on fit.
  3. Free 30-minute scoping callWe talk through scope, options and a realistic estimate — with no obligation.
Free estimateNo obligation

Talk to someone who builds this

Send a short brief and we will come back with an honest view and a realistic range.

Takes under a minute. We never share your details.

  • Free consultation
  • No commitment
  • NDA on request

Prefer to talk? Book a free 30-minute call →