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2 changes: 2 additions & 0 deletions blog/2025-09-04-chunking-strategies/index.mdx
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Expand Up @@ -382,6 +382,8 @@ When setting up a data ingestion pipeline for your RAG application, you often fa
- **Best for:** modular workflows where chunking is just one piece of the puzzle.
- **LlamaIndex:** Designed specifically for RAG pipelines. Its sophisticated [`NodeParsers`](https://docs.llamaindex.ai/en/stable/module_guides/loading/node_parsers/modules/) produce “Nodes,” optimized for ingestion and retrieval.
- **Best for:** high-performance, data-centric retrieval systems.
- **chonkie:** A lightweight, dedicated chunking library that focuses solely on splitting text. It offers a variety of chunking strategies such as [`SemanticChunker`](https://docs.chonkie.ai/python-sdk/chunkers/semantic-chunker) and is easy to integrate with other RAG libraries.
- **Best for:** projects where you want a simple, focused solution without the overhead of a larger framework.

### Manual Implementation

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