# How do you build a prompt library for your team?

Date: 2024-04-04 · Author: Robert Scales · https://guidetoartificialintelligence.com/en/learn/build-prompt-library-team/

**Answer:** A team prompt library is a shared, searchable collection of tested prompts, each stored with its purpose, the prompt text with fill-in blanks, a sample of good output, an owner and the date it was last tested. Start with the five or ten tasks your team repeats most, and only add prompts that someone has actually tried.

When several people on a team use ChatGPT or a similar assistant, each one slowly builds private know-how: the wording that gets a clean meeting summary, the instructions that keep a customer reply polite but firm. A prompt library turns that scattered know-how into a shared tool. New colleagues start from prompts that already work, results become more consistent, and nobody has to rediscover the same tricks.

The library itself can be simple. A shared spreadsheet, a page in your team wiki or a document folder works fine. What makes it useful is not the software but a consistent format and a rule that every entry has been tested.

## The six fields every entry needs

| Field | What goes in it | Why it matters |
|---|---|---|
| Name | A short, searchable label, such as "Meeting notes to action list" | People find prompts by task, not by clever titles |
| Purpose | One sentence: when to use it, and when not to | Prevents misuse on the wrong kind of task |
| Prompt | The full text, with blanks in [BRACKETS] | Lets anyone reuse it without rewriting |
| Example output | A short sample of a good result | Shows what "working" looks like |
| Owner | The person who maintains it | Someone is responsible for fixing it |
| Last tested | Date, and the tool or model used | Tells readers whether it may be stale |

Two optional fields help as the library grows: tags (department, task type) and a "known limits" note, such as "struggles with tables longer than 20 rows."

## A worked example entry

**Name:** Customer complaint reply, first draft
**Purpose:** Drafting a reply to a written complaint about a delay or error. Not for legal threats or refund disputes over [AMOUNT LIMIT]; send those to a manager.
**Owner:** [Name], customer service lead
**Last tested:** [date], ChatGPT

```prompt
You are a customer service writer for [COMPANY], a [TYPE OF BUSINESS]. Our tone is warm, direct and never defensive.

Write a reply to the customer message between the triple quotes.
- Start by acknowledging the specific problem in one sentence.
- Explain what happened in plain words, using only the facts I give below. Do not invent reasons or promises.
- State the next step and when the customer will hear from us.
- 120 words maximum. No exclamation marks.

Facts: [WHAT HAPPENED, WHAT WE WILL DO, BY WHEN]

"""
[PASTE CUSTOMER MESSAGE]
"""
```

**Example output:** a short, illustrative reply of about 100 words, pasted from a real test with customer details removed.

This entry follows advice from OpenAI's own prompting guide: put instructions first, separate the material to work on with markers such as triple quotes, be specific about length and format, and say what to do rather than only what to avoid. Notice also the line "Do not invent reasons or promises." A shared prompt will be used by people in a hurry, so it should carry its safety rules inside it.

## Where to keep it

Start where your team already works. A spreadsheet with one row per prompt is easy to sort and filter. A wiki page per prompt allows longer examples. Whatever you choose, keep one copy, linked from one place, so there are no competing versions.

If your team uses ChatGPT Team, the workspace plan OpenAI launched in January for groups of up to 149 people, members can also build custom GPTs and share them within the team. A GPT is a saved version of ChatGPT with its own instructions. Your most-used library entries can become team GPTs, so colleagues click a name instead of pasting a long prompt. OpenAI says it doesn't train its models on Team data or conversations, which matters when prompts involve work material. Keep the written entry in the library anyway, as the master copy and documentation.

A note on privacy: the library holds templates, not real data. Example outputs should be scrubbed of customer names and details before they are saved.

> **Try it:** List the three tasks you asked an AI to do most often last month. Write one library entry for each using the six fields above, test each prompt twice with different real inputs, and paste the better output as the example.

## How do you keep it up to date?

Give every prompt an owner and a test date, then use them. Once a quarter, or whenever your AI tool announces a new model, each owner reruns their prompts on the same saved test input and compares the result with the stored example. If the output is still good, update the "last tested" date. If it got worse, fix the wording and note what changed. Keeping one fixed test input per prompt makes this comparison quick and fair.

Make feedback easy. Add a column or a comment thread where anyone can write "this failed on…" with a short description. Owners review those notes during the regular check. Users are your best early warning, because they see failures the day they happen.

Retire prompts as willingly as you add them. If nobody has used an entry in months, or a new prompt does the same job better, archive the old one with a note pointing to its replacement. A short library of prompts that work is worth more than a long one nobody trusts.

## Sources

- Best practices for prompt engineering with OpenAI API: https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api
- OpenAI debuts ChatGPT subscription aimed at small teams: https://techcrunch.com/2024/01/10/openai-launches-chatgpt-subscription-aimed-at-small-teams/