> For the complete documentation index, see [llms.txt](https://docs.fastrouter.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fastrouter.ai/prompt-library.md). # Prompt Library ## Introduction Prompt Library is the single place to write, store, version, test, and optimize your prompts before deploying them to production. Instead of hard-coding prompt strings into your application, you author them in FastRouter, manage them as versioned records, and reference them by ID in your API calls. When you ship a change, you publish a new version — no code deploy required. This page covers what Prompt Library does, how it works, and how to call a stored prompt from the API. ### Why use it Prompts change far more often than application code. Keeping them in Prompt Library gives you: * **A versioned history** of every prompt, with notes on what changed in each version. * **Safe rollouts** — mark exactly one version as *Production* and have all live requests use it, then roll back instantly by promoting an older version. * **Optional optimization** — refine a system prompt with GEPA and save the result as a new version, without overwriting your original. * **Decoupled deployments** — update the prompt your app runs without redeploying the app, since requests reference the prompt by ID. ### How it works The lifecycle is: author a prompt in the **Prompt Library**, optionally **optimize** it, save changes as a **new version**, then **reference it by ID** from your application.

Prompt Library flow: Prompt Library → (optional) Prompt Optimization → New Version → Your App

1. **Create version** — Write and store a prompt in the Prompt Library. 2. **Optimize** (optional) — Run GEPA to refine the system prompt. 3. **Save optimized prompt** — The refined prompt is saved as a new version. 4. **Call by ID** — Your application references the prompt by its ID in API calls. ### Creating a prompt From **Prompts → Prompt Library**, click **Create Prompt** and fill in: * **Prompt Name** — A human-readable name (e.g. `Health Assistant Prompt`). * **Tags** — Up to five comma-separated tags for organization (e.g. `health`). * **Prompt** — The system prompt text. Use `{{curly braces}}` to insert variables that you fill in at call time. * **What changed in this version?** — A short changelog note (e.g. `Initial draft`). This is required and builds your version history. * **Set as "Production"** — When checked, this version is used for all live requests that reference the prompt by ID.

New Prompt form with name, tags, prompt body, version note, and Set as Production toggle

Saving creates **v1** of the prompt and assigns a permanent prompt ID (e.g. `pmpt_3c743f7f9f9e467eae6525f00e6e0650`). The ID never changes across versions — it's the stable handle your application uses.

Prompt Details showing v1 as the only version, marked Latest

### Versioning Each prompt keeps an ordered list of versions in the left panel of **Prompt Details**. Click **Add New** to create a new version, or generate one through optimization. Every version records its author, timestamp, and change note. The most recent version is tagged **Latest**; the version you promote is served as **Production**. ### Optimizing a prompt (optional) Click **Optimize** on any prompt to run GEPA based [Prompt Optimizations](/prompt-optimizations.md) against the current version. GEPA refines the system prompt and saves the result as a new version tagged **Optimized**, leaving your original untouched so you can compare or revert.

Prompt Details showing v2 (Latest, Optimized) alongside v1, with the expanded optimized prompt text

The optimized version is annotated with the optimizer job that produced it (e.g. `Optimized via optimizer job opt_094b50e343624dad99d127f4d57b28d7`), so you can trace any version back to its source. Use **Compare** to view the diff between two versions before promoting one to Production.

Compare Prompt Versions showing the differences between two prompt versions

### Calling a prompt by ID Reference a stored prompt by passing its `prompt_id` to the chat completions endpoint. When you do this **without pinning a version, FastRouter serves the version currently marked Production** — so promoting a new Production version changes what your app runs, with no code change on your side. The **API Usage** tab on the Prompt Details page generates a ready-to-run snippet for the selected prompt:

API Usage tab that shows the configuration to be used in your requests

```bash curl -X POST "https://api.fastrouter.ai/api/v1/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer API-KEY" \ -d '{ "model": "openai/gpt-4.1", "prompt_id": "pmpt_dde193a153a14fbf9829dd9499bf828b", "variables": {}, "messages": [ { "role": "user", "content": "hi" } ] }' ``` #### Request parameters | Parameter | Required | Description | | ----------- | -------- | ----------------------------------------------------------------------------------------------------------------------- | | `model` | Yes | The model to route the request to (e.g. `openai/gpt-4.1`). | | `prompt_id` | Yes | The stored prompt's ID. Resolves to the **Production** version. | | `variables` | No | Key–value map filling any `{{variables}}` declared in the prompt. Defaults to `{}`. | | `messages` | Yes | The conversation turns. The stored prompt is applied as the system prompt; `messages` carries the user/assistant turns. | #### Working with variables If your prompt contains placeholders such as `{{patient_age}}` or `{{topic}}`, supply their values in `variables`: ```json { "model": "openai/gpt-4.1", "prompt_id": "pmpt_dde193a153a14fbf9829dd9499bf828b", "variables": { "topic": "ibuprofen dosing" }, "messages": [ { "role": "user", "content": "Is this safe with my blood pressure medication?" } ] } ``` FastRouter substitutes the values into the stored prompt before sending the request to the model. Detected variables for a prompt are listed at the bottom of the **Prompt** tab. ### Promoting and rolling back To change what production traffic uses, open the target version and set it as **Production** (or check **Set as "Production"** when saving a new version). Because your application references the prompt only by `prompt_id`, the switch takes effect immediately for all live requests, and you can revert by promoting a previous version the same way. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.fastrouter.ai/prompt-library.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.