# Advanced Topics
This section covers advanced usage patterns and customization options.
## Topics
- [Custom Chunkers](custom-chunkers.md) - Implement your own chunking strategies
- [Custom Embedders](custom-embedders.md) - Create custom embedding providers
- [LLM Chunking](llm-chunking.md) - Configure and use LLM-based chunking
- [Performance](performance.md) - Optimization and tuning tips
## When to Use Advanced Features
### Custom Chunkers
Use custom chunkers when:
- Built-in strategies don't fit your use case
- You need domain-specific chunking (e.g., code, legal text)
- You want to integrate with external NLP libraries
### Custom Embedders
Use custom embedders when:
- You need a model not supported by FastEmbed
- You want to use external embedding APIs
- You need custom preprocessing or postprocessing
### LLM Chunking
Use LLM chunking when:
- Semantic coherence is critical
- You have access to LLM infrastructure
- Quality matters more than speed/cost
### Performance Tuning
Focus on performance when:
- Processing large volumes of data
- Running on resource-constrained systems
- Optimizing for production workloads