In most teams, prompts start life as chat snippets: someone tries a few variations until the answer “looks good enough”, then pastes that into a document or an issue. It works once, but it is hard to repeat. When the model changes or the teammate changes, quality drops again.
In boomPrompt we treat prompt design more like API design: a careful contract between humans and the model. In this article we will walk through a simple but robust framework you can use to turn fuzzy ideas into prompts that behave predictably in production.
1. Start from the real job‑to‑be‑done
Before writing a single word of a prompt, write down the real job‑to‑be‑done in plain language: who is using this, what input do they have, what output will actually be used in the product. “Summarise this text” is not enough – you need to know for whom and for what.
For example, instead of “improve this landing page copy”, a better job description is: “generate three alternative hero sections for a B2B SaaS landing page targeting heads of marketing at mid‑size companies, optimised for clarity and trust rather than hype”.
2. Express context, goal and constraints explicitly
High‑quality prompts make three things very clear:
- Context: what the model should know about the product, audience and channel.
- Goal: how we will judge success – click‑through, comprehension, tone, structure.
- Constraints: format, length, language, things to avoid, and any compliance or brand rules.
In boomPrompt templates we usually dedicate separate sections or bullet lists to each of these, so they are easy to scan and update over time. This also makes it easier to map fields to UI forms when you integrate prompts into your own tools.
3. Design the output format as if it were an API response
Instead of asking for a free‑form paragraph, think about the downstream consumer of the output. Does another script parse it? Does a marketer paste it into an editor? Is it going into a spreadsheet or database?
Specify clear section headings, bullet lists, JSON fields or Markdown tables depending on how the output will be used. When you later change models, this framing dramatically reduces the effort needed to keep your integration working.
4. Use examples sparingly but deliberately
Examples are powerful, but they should be chosen carefully. One or two concise, high‑signal examples that mirror your real workflows are better than a long list of generic ones. In boomPrompt we often add a single “good” example plus a short explanation of why it is good, rather than dozens of variations.
5. Iterate with real inputs, not toy data
Once you have a first version of the prompt, test it with real inputs from your analytics, CRM, support tickets or content backlog – anything close to what the model will see in production. This is where you will discover edge cases, missing constraints and ambiguous language.
We recommend keeping a small “prompt lab” document where you record bad outputs and how you adjusted the prompt to fix them. Over time this becomes a valuable knowledge base for your team.
6. Turn your best prompts into shared templates
When a prompt has survived multiple projects, move it into a shared dictionary category inside boomPrompt. Give it a clear title, add tags and document when teammates should (and should not) use it. This is how you gradually move from improvisation to a more reliable prompt “standard library” for your organisation.
If you want to see how this framework looks in practice, browse the boomPrompt dictionary and explore a few categories that match your role.