Building an Effective Knowledge Base with AI

## Why Your Knowledge Base Structure Matters

A well-organized knowledge base is the foundation of any effective AI-powered retrieval system. In this tutorial, we'll walk through the key principles for building a knowledge base that works seamlessly with AI.

### Document Organization

Structure your documents hierarchically:

```
knowledge-base/
├── products/
│ ├── product-a/
│ │ ├── overview.md
│ │ ├── features.md
│ │ └── faq.md
│ └── product-b/
├── support/
│ ├── troubleshooting/
│ └── guides/
└── company/
├── policies/
└── procedures/
```

### Metadata is Key

Rich metadata enables better filtering and retrieval:

```yaml
---
title: Product A Overview
category: products
tags: [product-a, overview, features]
last_updated: 2024-12-01
audience: [customers, sales]
---
```

### Writing for AI

AI retrieval works best with:

- **Clear headings**: Use descriptive H2 and H3 tags
- **Complete sentences**: Avoid bullet points without context
- **Self-contained sections**: Each section should make sense alone
- **Explicit relationships**: Link related concepts clearly

### Maintenance Best Practices

- Review and update quarterly
- Track document usage and accuracy
- Remove outdated information
- Version control everything

### Getting Started with Notir

Upload your knowledge base to Notir in minutes:

```bash
notir upload ./knowledge-base --recursive
```

That's it! Your AI-powered knowledge base is ready to use.