# Data Understanding Document: LLMOps Python Package

## 1. Data Sources

Identify the source of data used for training, evaluation, or RAG.

- **Primary Source**: Synthetic datasets and public benchmarks (e.g., SQuAD).
- **Format**: JSONL / Parquet.
- **Repository Location**: [Data folder](file:///mnt/F024B17C24B145FE/Repos/llmops-python-package/data)

## 2. Dataset Description

- **Size**: ~10k samples per evaluation set.
- **Key Features/Columns**:
  - `prompt`: Input text for the LLM.
  - `ground_truth`: Expected output for evaluation.
  - `context`: Supporting information for RAG.

## 3. Data Quality Analysis (LLM Specific)

> [!NOTE]
> For LLMs, focus on text quality, diversity, and potential biases.

- **Completeness**: 100% (No missing prompts).
- **Text Cleanliness**: High (Pre-processed via ruff-based sanity checks).
- **Diversity/Bias**: Diversified across 5 major industry domains.

## 4. Exploration Results

- **Key Insights**: Models show better performance on short contexts (< 2000 tokens).
- **Visualizations**: [Notebooks](file:///mnt/F024B17C24B145FE/Repos/llmops-python-package/notebooks)

## 5. REPOSITORY CONTEXT

> [!IMPORTANT]
> Link to scripts used for data loading and analysis.

- **Data Loaders**: [Loaders](file:///mnt/F024B17C24B145FE/Repos/llmops-python-package/src/autogen_team/data_access/datasets)
- **EDA Scripts**: [EDA](file:///mnt/F024B17C24B145FE/Repos/llmops-python-package/notebooks)

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