# AI Prompt‑Engineering Dashboard

## Dashboard / How to use  
A single‑page overview that lets prompt engineers track every prompt, its revisions, test outcomes, and ROI. Ideal for LLM product teams, solo AI consultants, and anyone who iterates prompts at scale.  

**Quick start**  
- Duplicate this template into your workspace.  
- Open the **Prompt Library** table, add your first prompt (title + description).  
- Run a test in **Test Runs** and link the new prompt.  
- Review the automatically calculated **ROI** in the Metrics view.  
- Share the dashboard with stakeholders via Notion’s share link.  
- Use the **"Publish Prompt"** button to generate a shareable link for external showcasing or monetization.

## Prompt Library – Central catalog of all prompts  
Store each prompt with its purpose, version, and status. Use the linked **Version History** relation to see every change.

| Prompt (Title) | Description | Category | Status | Current Version | Owner | Tags | Last Updated |
|---|---|---|---|---|---|---|---|
| Summarize Article | Generates a concise 3‑sentence summary of a news article. | Summarization | ✅ Active | v1.3 | Alice | #summary, #news | 2024‑06‑28 |
| Generate Product Copy | Writes SEO‑optimized product descriptions (≤150 words). | Copywriting | ⚙️ In Review | v0.9 | Bob | #ecommerce, #seo | 2024‑06‑20 |
| Code Refactor Helper | Suggests refactored Python code with PEP‑8 compliance. | Code | ✅ Active | v2.0 | Carol | #dev, #python | 2024‑06‑25 |

> 💡 **Tip:** Use the **Category** select to group prompts for quick filtered views (e.g., “Summarization”, “Copywriting”, “Code”).  

> ✅ **Best practice:** Keep the **Owner** field up‑to‑date so responsibility is clear when prompts are edited.

### Property guide (convert to Notion)  
- **Prompt** – Title (Title)  
- **Description** – Text (Multi-line)  
- **Category** – Select (Summarization, Copywriting, Code, etc.)  
- **Status** – Select (Active, In Review, Deprecated)  
- **Current Version** – Text (e.g., “v1.3”)  
- **Owner** – Person  
- **Tags** – Multi‑select  
- **Last Updated** – Date (Last edited time)

## Version Control & Changelog – Track every edit  
Every time a prompt is updated, create a new entry here. Link back to the Prompt Library entry to see the full lineage.

| Change ID (Title) | Prompt (Relation) | Version | Change Summary | Changed By | Date | Approved |
|---|---|---|---|---|---|---|
| CHG‑001 | Summarize Article | v1.3 | Added bullet‑point output option. | Alice | 2024‑06‑28 | ✅ |
| CHG‑002 | Generate Product Copy | v0.9 | Switched tone to “friendly”. | Bob | 2024‑06‑20 | ⚙️ |
| CHG‑003 | Code Refactor Helper | v2.0 | Integrated Pylint suggestions. | Carol | 2024‑06‑25 | ✅ |

> 💡 **Tip:** Filter this view by **Approved** = ✅ to see only production‑ready versions.

### Property guide  
- **Change ID** – Title (Title)  
- **Prompt** – Relation → Prompt Library  
- **Version** – Text (e.g., “v1.3”)  
- **Change Summary** – Text (Multi-line)  
- **Changed By** – Person  
- **Date** – Date (Created time)  
- **Approved** – Select (✅ Approved, ⚙️ Pending)

## Test Runs – Execute and record results  
Run prompts against sample inputs, capture metrics, and link each run to its prompt version. Integrate OpenAI API calls to automate token usage and latency recording.

| Test Run (Title) | Prompt (Relation) | Input Sample | Output Snapshot | Tokens Used | Success Rate | Run Date | Notes |
|---|---|---|---|---|---|---|---|
| RUN‑001 | Summarize Article | “NY Times article on climate…” | “Three‑sentence summary …” | 120 | 92% | 2024‑06‑29 | Good for news feeds. |
| RUN‑002 | Generate Product Copy | “Eco‑friendly water bottle” | “Stay hydrated …” | 210 | 85% | 2024‑06‑22 | Needs stronger CTA. |
| RUN‑003 | Code Refactor Helper | “def foo(): pass” | “def foo():\n    # TODO …” | 95 | 98% | 2024‑06‑26 | Passed lint. |

> ✅ **Best practice:** Add a **Success Rate** formula that compares expected vs. actual outcomes (e.g., `if contains(output, “keyword”) then 100 else 0`).  

### Property guide  
- **Test Run** – Title (Title)  
- **Prompt** – Relation → Prompt Library  
- **Input Sample** – Text (Multi-line)  
- **Output Snapshot** – Text (Multi-line)  
- **Tokens Used** – Number  
- **Success Rate** – Number (0‑100)  
- **Run Date** – Date (Created time)  
- **Notes** – Text (Multi-line)

## ROI & Metrics – Measure impact per prompt  
Aggregates data from Test Runs and business KPIs (e.g., conversion lift). Use roll‑up properties to auto‑calculate totals. Implement a cost calculator that pulls pricing from OpenAI’s API for real-time ROI calculations.

| Prompt (Relation) | Avg Success Rate | Avg Tokens | Monthly ROI ($) | Last Test Run | Owner |
|---|---|---|---|---|---|
| Summarize Article | 92% | 118 | 1,200 | RUN‑001 (2024‑06‑29) | Alice |
| Generate Product Copy | 85% | 210 | 2,800 | RUN‑002 (2024‑06‑22) | Bob |
| Code Refactor Helper | 98% | 95 | 0 (internal) | RUN‑003 (2024‑06‑26) | Carol |

> 💡 **Tip:** Link this table to a **Finance** database (optional) to pull actual revenue numbers via roll‑up.

### Property guide  
- **Prompt** – Relation → Prompt Library (Show as title)  
- **Avg Success Rate** – Rollup (Success Rate, average)  
- **Avg Tokens** – Rollup (Tokens Used, average)  
- **Monthly ROI ($)** – Number (manual or formula)  
- **Last Test Run** – Rollup (Run Date, latest)  
- **Owner** – Person (from Prompt Library)

## Automation & Integration – Keep the workflow alive  
- **Formula**: `if(prop("Success Rate") >= 90, "✅", "⚠️")` adds a visual flag in Test Runs.  
- **Zapier/Make**: Trigger a new Test Run when a prompt version is marked **Approved**.  
- **Notion API**: Export prompt JSON for external LLM orchestration tools. Automate OpenAI calls from the Test Runs table to track token usage and latency.

> ✅ **Best practice:** Schedule a weekly “Prompt Review” view (filtered by **Status** = In Review) and assign it to a rotating team member.

## How to set up in Notion  
1. Click **Duplicate** at the top‑right of this page to add the template to your workspace.  
2. For each markdown table, type `/database` → **Table – Full page**, then copy‑paste the rows. Notion will prompt you to set the **Title** column – choose the column indicated in each table’s guide.  
3. Convert the remaining columns to the property types listed (Select, Multi-select, Person, Date, Number, Formula, Rollup, Relation).  
4. Create the **Relation** properties between Prompt Library, Version Control, Test Runs, and ROI tables as described.  
5. Add the **Formula** and **Rollup** properties using the exact formulas provided in the Automation & Integration section.  
6. (Optional) Set up Zapier/Make integrations: use the Notion API token, select the “Version Control → Approved” trigger, and map to a new Test Run entry.  
7. Click **Share → Share to web**, enable **Allow duplicate as template**, and copy the public link to sell or distribute.  
8. Utilize the **"Publish Prompt"** button to create a shareable link for external use.

Your AI Prompt‑Engineering Dashboard is now ready to catalog, version, test, and monetize every LLM prompt with professional‑grade rigor. Enjoy!