Some days, AI feels like magic. Just looking at the output generated by some LLM give me Endorphines. Other days, it’s a lot of typing for not much result, and everyone does it differently. If you understand Large Language Models (LLMs), you will know that they are designed to make predictions based on the context provided. This means, the more contextually rich our input or prompt is, the better the prediction or output will be. That’s where prompt templates win. They’re not hype. They’re the rails the train needs to move at all.
đź’ Why prompt templates?
Consistency isn’t an accident. It’s the result of habits, structure, and a little discipline. Prompt templates give you a starting point that is never zero. They reduce friction. They save time. Most of all, they make quality repeatable. Same task, same structure, reliably solid output. New teammates don’t have to guess “how we do it here”; they pick a template that worked in practice. Suddenly prompts aren’t gut feeling anymore. They’re artifacts you can discuss, improve, and version. That’s the moment AI turns from toy into tool.
🧱 What are they, really?
A prompt template is basically a prompt you'd usually type in the chat box of your LLM. But instead of starting from scratch, you get a scaffold. Think of it like writing a CV in MS Word—you wouldn't kick things off with a blank page. You'd start with a template. That's what prompt templates are all about. Not a rigid form—more like a clear frame with placeholders. Context. Task. Constraints. Output format. Here are some guardrails you could consider regarding what a prompt template could include:
- Context: what this is about, who it’s for, which code/module matters.
- Task: what exactly should happen? Refactor. Review. Generate. Explain.
- Constraints: rules and limits—style, length, DACH compliance, tech stack.
- Output format: list, table, diff, code block with comments.
This structure kills the biggest problem: vague prompts. It makes sure the model doesn’t have to guess. It forces us to think cleanly before we generate. That’s valuable.
đź“‚ How to organize them in a team
Short version: treat them like code. Long version: versioned, reviewable, discoverable.
If you're a software developer, go ahead and create a repo—or a cool folder in your monorepo—called ai-prompts/. Inside, set up a simple structure: /reviews, /tests, /refactoring, /docs, /ops. Each file should be in Markdown with a small YAML header at the top: name, version, owner, tags. Make sure changes go through merge requests. Review prompts just like you would code. Keep a changelog and assign ownership. Most IDEs can explore existing templates if they're in the right spot (check out VSCode for example: https://code.visualstudio.com/docs/copilot/customization/prompt-files).
If you're not a dev, just whip up a simple text file and stash it somewhere. That's the basics. It's also a good idea to use versioning and share it with your colleagues.
I keep my prompt templates normally within the actual projects I'm working on. But I also have my every available template in a public repo: https://github.com/zeekrey/prompts
Why so “strict”? Because prompts that die in a chat thread help no one. Prompts that live in a repo become part of the system. They can evolve. They can be measured. They can be found via tags like task:review, stack:react, risk:high. And they integrate nicely: as IDE snippets, as a Raycast template, as a CLI command in your tooling.
It's also good to add a short policy page (no sensitive data, respect licenses, follow model policy). Add an index/README that explains how to find and use templates. Maybe a tiny “how to contribute.” Lower the bar to participate.
🌱 How to start
I personally like to start with code review assist. But there are other ideas you could start with as well:
Code Review
Let the model do a pre-check: readability, tests present, naming consistent, hotspots and edge cases flagged. The output isn’t a verdict; it’s a checklist or a diff you can comment on. Reviewers save time but keep control.
Test generation and augmentation
Unit or integration scaffolds that give you a runway: Given/When/Then, typical fixtures, a few edge cases. No magic. Just momentum and fewer gaps.
Refactoring sketches
Not “rewrite everything,” but small, safe steps: identify risks, propose a sequence, point to impacted modules. Progress without the big bang.
Docs and README
Summarize a module, document the public API, state assumptions, include a minimal example. Newcomers ramp up faster. Architecture discussions. Options A/B/C with pros, cons, risks, and open questions. End with a brief recommendation—or at least a clear map for the conversation. Not a final answer. A catalyst.
đź’ˇ Conclusion
Speed alone isn’t enough. We need speed with direction. Prompt templates give teams a shared language to work with AI reliably. They make the work visible, reviewable, and shareable. They capture team intelligence inside prompts, so you don’t start from scratch every time. You can start small. One folder. Three templates. A short README. Then grow. Iteration beats perfection. And one day you notice: the debate isn’t whether AI “works” anymore. It’s which template we’ll improve today. That’s a good sign. That’s progress.