llm-data-automation

# LLM Data Automation for Construction

## Overview

Based on DDC methodology (Chapter 2.3), this skill enables automation of construction data processing using Large Language Models (LLM). Instead of manually coding data transformations, you describe what you need in natural language, and the LLM generates the necessary Python/Pandas code.

**Book Reference:** "Pandas DataFrame и LLM ChatGPT" / "Pandas DataFrame and LLM ChatGPT"

> "LLM-модели, такие как ChatGPT и LLaMA, позволяют специалистам без глубоких знаний программирования внести свой вклад в автоматизацию и улучшение бизнес-процессов компании."
> — DDC Book, Chapter 2.3

## Quick Start

### Option 1: Use ChatGPT/Claude Online
Simply describe your data processing task in natural language:

```
Prompt: "Write Python code to read an Excel file with construction materials,
filter rows where quantity > 100, and save to CSV."
```

### Option 2: Run Local LLM (Ollama)
```bash
# Install Ollama from ollama.com
ollama pull mistral

# Run a query
ollama run mistral "Write Pandas code to calculate total cost from quantity * unit_price"
```

### Option 3: Use LM Studio (GUI)
1. Download from lmstudio.ai
2. Install and select a model (e.g., Mistral, LLaMA)
3. Start chatting with your local AI

## Core Concepts

### DataFrame as Universal Format
```python
import pandas as pd

# Construction project as DataFrame
# Rows = elements, Columns = attributes
df = pd.DataFrame({
    'element_id': ['W001', 'W002', 'C001'],
    'category': ['Wall', 'Wall', 'Column'],
    'material': ['Concrete', 'Brick', 'Steel'],
    'volume_m3': [45.5, 32.0, 8.2],
    'cost_per_m3': [150, 80, 450]
})

# Calculate total cost
df['total_cost'] = df['volume_m3'] * df['cost_per_m3']
print(df)
```

### LLM Prompts for Construction Tasks

**Data Import:**
```
"Write code to import Excel file with construction schedule,
parse dates, and create a Pandas DataFrame"
```

**Data Filtering:**
```...