one of the Codia AI Code technologies: Intelligent Naming Technology
Tags: figma, ai, webdev, mobile
Published: 2024-02-16 15:27:59

## 1. Introduction
The articles [Codia AI: Shaping the Design and Code Revolution of 2024](https://dev.to/happyer/codia-ai-shaping-the-design-and-code-revolution-of-2024-1jf3) and [Codia AI: Shaping the Design and Code Revolution of 2024 - Part 2](https://dev.to/happyer/codia-ai-shaping-the-design-and-code-revolution-of-2024-part-2-2iak) introduced Codia AI, which possesses its own AI naming model. The code variables and functions generated by Codia AI Code adhere to naming conventions and semantics. This paper will focus on the intelligent naming technology for converting Figma design drafts into code.

![Figma to Swift](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/v9s3ma3yqry55hq3fvid.png)

## 2. Implementation Principle

1. **Data Preparation and Preprocessing**: Collect a large amount of naming data for design elements, including but not limited to components and layers in Figma design files. These data are preprocessed to format suitable for model training.
2. **Model Training**: Use LLMs and fine-tune them according to the characteristics and contextual information of design elements to improve the accuracy and relevance of naming.
3. **Context Understanding**: The model must be able to parse the DOM structure of Figma files, understand the hierarchical and logical relationships between various design elements, and thereby generate more accurate and meaningful names.

## 3. Technical Details

### 3.1. Implementation Details
- **Feature Extraction**: Extract key information from the design drafts, such as color, shape, and layout position, as part of the model input.
- **Semantic Understanding**: Utilize LLMs to analyze the function and purpose of design elements semantically, in order to generate names that are descriptive and easy to understand.

### 3.2. Process

1. Design Draft Upload and Parsing: Upload the Figma design draft, parse its structure, and extract element features.
2. Data Preprocessing: Standardize the extracted features to prepare model training data.
3. Model Training: Train or fine-tune LLM based on the preprocessed data.
4. Naming Generation: Input design element features, and the model generates intelligent naming.
5. Result Verification and Adjustment: Correct the format of the generated names, etc.

### 3.3. Code Implementation (Example)

The following Python code example demonstrates how to use the LLM API to generate naming for design elements:

```python
import openai

openai.api_key = 'your-api-key-here'

def generate_name(element_features):
    response = openai.Completion.create(
        model="your model",  
        prompt=f"Generate a creative name for a design element with the following features: {element_features}",
        temperature=0.5,
        max_tokens=60,
        top_p=1.0,
        frequency_penalty=0.0,
        presence_penalty=0.0
    )
    return response.choices[0].text.strip()

# Example: Generate a name for a button with specific features
element_features = "Button, primary color blue, located at the bottom of the page, used for submitting forms"
generated_name = generate_name(element_features)
print(generated_name)
```

## 4. Technical Optimization

To further improve the accuracy and efficiency of intelligent naming, the following optimization measures can be adopted:

### 4.1. Model Optimization

- **Training Data Set**: Use a larger and more diverse training dataset to train the LLM, enhancing its generalization ability.
- **Algorithm Optimization**: Adopt more advanced machine learning algorithms, such as deep learning or reinforcement learning, to improve model performance.
- **Refined Context Analysis**: Further optimize the model's understanding of the context of design elements to generate more precise and specific naming.
- **Multimodal**: Further refine the context of naming through multimodality.

### 4.2. Optimization for Exceeding Model Token Limits

When using intelligent naming technology for Figma design drafts, one may encounter issues where the Figma tree is too large, and the request content exceeds the model's token limits. To address this issue, the following optimization measures can be adopted:

#### 4.2.1. Design Draft Chunking

Chunking large design drafts for processing is an effective optimization method. By dividing the design draft into smaller chunks, each chunk can be processed one at a time, thus avoiding sending too much data in a single request and exceeding token limits. The steps for chunking processing are as follows:

1. **Design Draft Preprocessing**: Before sending the DOM structure, preprocess the design draft by dividing it into several smaller chunks, such as lists and blocks, through an AI model.
2. **Chunk Sending**: Send the DOM structure of each chunk to the AI naming service separately.
3. **Chunk Processing**: The AI naming service processes each chunk separately and generates its naming result.
4. **Result Merging**: Merge all chunks' naming results to form a

 complete design draft naming result.

#### 4.2.2. Concurrent Requests

After the design draft has been chunked for processing, multiple requests can be sent simultaneously instead of sequentially. This significantly reduces the total processing time. The steps for concurrent requests are as follows:

1. **Concurrent Sending**: Send the DOM structures of multiple chunks to the AI naming service simultaneously.
2. **Concurrent Processing**: The AI naming service processes multiple requests at the same time.
3. **Result Collection**: Collect the naming results of all concurrent requests.
4. **Result Merging**: Merge the collected results to form a complete design draft naming result.

#### 4.2.3. Code Implementation (Example)

Below is a simplified code implementation for design draft chunking and concurrent requests:

```javascript
// Figma plugin code
async function smartNamingWithConcurrency() {
  const domStructureBlocks = getDomStructureBlocksFromDesignFile();
  const namingResults = await Promise.all(domStructureBlocks.map(block => {
    return aiRenameService.processDomStructure(block);
  }));
  const mergedNamingResult = mergeNamingResults(namingResults);
  applyNamingResultToDesignFile(mergedNamingResult);
}

// AI naming service code
function processDomStructure(domStructureBlock) {
  const filedNodeId = convertDomStructureToNodeId(domStructureBlock);
  the namingResult = llmModel.analyzeDomStructure(filedNodeId);
  return namingResult;
}
```

### 4.3. Others
Additionally, there are optimizations for output result format, input and output length, etc.

## 5. Conclusion
This paper introduced the intelligent naming technology for generating code from Figma design drafts. This technology automates the naming of design elements through AI, enhancing naming consistency as well as the usability and quality of the code. Initially, the paper outlined the principles of data preparation, model training, and context understanding. Then, it detailed the technical implementation, including feature extraction, semantic understanding, and the process steps. Example code showed how to use LLM to generate naming for design elements. Lastly, the paper discussed methods for technical optimization, including model optimization, token optimization, etc., to improve the efficiency and accuracy of intelligent naming.