> ## Documentation Index
> Fetch the complete documentation index at: https://prompt.university/llms.txt
> Use this file to discover all available pages before exploring further.

# Contributing to Research

> How to contribute model discoveries, papers, and tasks

# Contributing to Open Research

We welcome contributions from the community! This guide explains how to add model discoveries, research papers, and applied tasks to our database.

## Ways to Contribute

<CardGroup cols={2}>
  <Card title="Model Discoveries" icon="robot" href="#model-discoveries">
    Found a new AI model on social media? Add it!
  </Card>

  <Card title="Research Papers" icon="file-text" href="#research-papers">
    Share papers that impact model performance
  </Card>

  <Card title="Prompt Examples" icon="message">
    Viral prompts with engagement data
  </Card>

  <Card title="Applied Tasks" icon="code" href="#applied-tasks">
    Document problem-solving workflows
  </Card>
</CardGroup>

## Model Discoveries

### What We Track

When you discover a new AI model, document:

```yaml theme={null}
Required:
  - name: "Model Name"
  - provider: "OpenAI | Anthropic | Google | etc"
  - source_url: "Link to announcement"
  - platform: "x | instagram | linkedin | arxiv"
  - posted_date: "YYYY-MM-DD"
  
Optional but valuable:
  - engagement:
      likes: 45000
      shares: 12000
      views: 2500000
      bookmarks: 8900
  - capabilities: ["chat", "vision", "audio"]
  - description: "What the model does"
```

### How to Submit

1. **Fork the repository**
2. **Create a new file** in `research/models/` using the template
3. **Fill in the frontmatter** with all available data
4. **Add social proof** - screenshots or links to announcements
5. **Submit a PR** with description of discovery source

### Template

```mdx theme={null}
---
title: "Model Name"
description: "Brief description"
---

# Model Name

<Note>
  **Status**: 🔵 Discovered  
  **Source**: X/Twitter | LinkedIn | Instagram | arXiv
</Note>

## Overview

| Property | Value |
|----------|-------|
| **Provider** | Company Name |
| **Release Date** | YYYY-MM-DD |
| **Modalities** | Text, Image, Audio |

## Discovery Source

<Card>
  **Platform**: X (Twitter)  
  **Author**: @username  
  **Posted**: Date  
  **Engagement**: X likes | Y shares | Z views | W bookmarks
  
  > "Quote from the announcement"
  
  [View Original →](link)
</Card>

## Claims to Validate

- [ ] Context window size
- [ ] Multimodal capabilities
- [ ] Benchmark scores
- [ ] Pricing information

## Related Models

- [Related Model](/research/models/related-model)
```

## Research Papers

### What Makes a Good Paper Contribution

We prioritize papers that:

* **Directly impact deployed models** (e.g., improves GPT-4o by X%)
* **Introduce new benchmarks** or evaluation methods
* **Challenge existing assumptions** (negative results welcome!)
* **Have reproducible results** with available code

### Impact Levels

<AccordionGroup>
  <Accordion title="🔴 Revolutionary">
    Paradigm shift. Examples: Transformers, GPT-3 moment, breakthrough architecture
  </Accordion>

  <Accordion title="🟠 Significant">
    Major measurable improvement (>10% on key benchmark)
  </Accordion>

  <Accordion title="🟡 Incremental">
    Small but meaningful improvement (2-10%)
  </Accordion>

  <Accordion title="🟢 Validation">
    Reproduces or confirms existing work
  </Accordion>

  <Accordion title="⚪ Negative">
    Challenges or disproves existing claims (valuable!)
  </Accordion>
</AccordionGroup>

### Required Information

```yaml theme={null}
Required:
  - title: "Paper Title"
  - authors: ["Author Name"]
  - source: "arxiv | neurips | icml | etc"
  - source_id: "2501.12345"
  - url: "https://arxiv.org/abs/2501.12345"
  - impact_level: "significant"
  
For model impacts:
  - affected_models: ["gpt-4o", "claude-3-opus"]
  - benchmark_changes:
      math: { old: 76.4, new: 87.9 }
```

## Applied Tasks

### Task Contribution Format

Document real-world problem-solving workflows:

```yaml theme={null}
Required:
  - title: "Task Name"
  - category: "content-creation | coding | analysis"
  - steps: ["Step 1", "Step 2", ...]
  - models_used: ["gpt-4o", "claude-3-opus"]
  
Optional:
  - cost: 0.47
  - quality_score: 8.5
  - studio: "Content Creation"
```

### Task Structure

Each task should include:

1. **Problem Definition** - What are we solving?
2. **Capability Mapping** - What AI capabilities are needed?
3. **Model Selection** - Which frontier models? Why?
4. **Step-by-Step** - Actual prompts and outputs
5. **Results** - Quality metrics, cost breakdown
6. **Insights** - What did we learn?

## Data Schema

All contributions follow our open schemas:

* **Models**: See [`university/schemas/model.md`](https://github.com/potentiallyai/prompt-university)
* **Papers**: See [`university/schemas/research.md`](https://github.com/potentiallyai/prompt-university)
* **Tasks**: See [`university/schemas/applied.md`](https://github.com/potentiallyai/prompt-university)

## Review Process

<Steps>
  <Step title="Submit PR">
    Create a pull request with your contribution
  </Step>

  <Step title="Automated Checks">
    Schema validation and link checking
  </Step>

  <Step title="Community Review">
    Other contributors can comment and suggest
  </Step>

  <Step title="Graduate Review">
    For model validations and paper analysis
  </Step>

  <Step title="Merge">
    Approved contributions are merged and deployed
  </Step>
</Steps>

## Discord Integration

Contributions feed into our [Discord community](https://club.prompt.university):

* Model discoveries → `#new-models` channel
* Paper impacts → `#research-updates` channel
* Task workflows → `#studio-showcase` channel

## Attribution

All contributors are credited:

* GitHub contributors list
* Page footers ("Contributed by @username")
* Monthly contributor highlights

## Code of Conduct

* **Be accurate** - Verify claims before submitting
* **Be transparent** - Disclose conflicts of interest
* **Be respectful** - Constructive feedback only
* **Be open** - Embrace negative results and corrections

## Questions?

* Open an [issue on GitHub](https://github.com/potentiallyai/prompt-university/issues)
* Join our [Discord](https://club.prompt.university)
* Email: [research@prompt.university](mailto:research@prompt.university)

***

*Thank you for helping democratize AI research!*