# Next-Gen Autolog

### Overview

Next-Gen Autolog takes documentation to the next level by integrating metadata analysis with code analysis, providing richer, more accurate, and context-aware insights. Powered by [Ask AI](https://docs.vectice.com/introduction/readme/askai), it captures not just what the code does, but how it performs in real-world scenarios—enhancing understanding, usability, and maintainability.

#### The power of Metadata and Code

By integrating metadata and code analysis, Next-Gen Autolog provides documentation which is:

✅ Context-aware – Explains what the code does and how well it works.\
✅ Insight-driven – Highlights model performance, dataset biases, and risks.\
✅ Actionable – Surfaces key insights to improve model trust and usability.

### Key capabilities

#### 1. Automated context extraction with GenAI-powered Autolog

Enrich captured metadata with automated contextual information extraction, improving documentation quality and usability.

* Structured asset organization: assets are systematically categorized into sections.
* Contextual insights: automatically integrates asset creation details and relevant insights.
* Automated lineage tracking: derives relationships between assets without manual input.
* Feature engineering logic extraction: captures and documents transformation processes applied to data.

#### 2. Enriched documentation

Leverage extracted metadata to create highly detailed and nuanced documentation.

* AI-Powered understanding: Ask AI uses extracted insights to interpret and explain code functionality.
* Accurate feature engineering documentation: Captures complex data transformations and feature creation steps.
* Densely populated lineage information: Provides a comprehensive view of data dependencies and transformations.

#### 3. Automated report generation using templates and macros

With enriched metadata, reports can be generated effortlessly using predefined templates and macros.

* Advanced prompting: Ask AI applies metadata-driven prompts to generate well-structured documentation.
* Comprehensive long-form reports: Create detailed and complex documents using pre-defined macros and widgets.

### How It Works

Use[ Autolog](https://docs.vectice.com/introduction/readme/autolog) to capture all metadata from your notebook. Once the metadata is logged into Vectice:

1. Navigate to the **Iteration Page.**
2. Click on **Organize with Ask AI**.
3. **Ask AI** will:

* Reorganize iteration content
* Add structured sections, descriptions, and notes
* Build lineage between assets
* Render dataset transformations

The results are immediately visible in the iteration page and can be integrated into the documentation using Macros, Widgets or pre-defined Prompts.

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