Answer: To break documents into smaller, meaningful segments for better retrieval

## Explanation Chunking is a critical preprocessing step in RAG (Retrieval-Augmented Generation) pipelines for several reasons: ### Why chunking is important: 1. **Better retrieval accuracy**: Breaking documents into smaller, meaningful segments allows for more precise retrieval of relevant information from the vector database. 2. **Context preservation**: Proper chunking maintains semantic coherence within each chunk, making embeddings more accurate. 3. **Efficient processing**: Smaller chunks are easier to embed and store in vector databases. 4. **Improved relevance**: When a user query comes in, the system can retrieve the most relevant chunks rather than entire documents. ### Why other options are incorrect: - **A. To improve GPU utilization**: While chunking might indirectly affect GPU usage, this is not its primary purpose. - **C. To reduce costs of vector DBs**: Chunking doesn't necessarily reduce costs; it might even increase storage needs due to more embeddings. - **D. To avoid the need for embeddings**: Chunking doesn't eliminate the need for embeddings; in fact, each chunk still needs to be embedded for retrieval. ### Best practices for chunking: - Use semantic boundaries (paragraphs, sections) - Consider overlap between chunks to maintain context - Adjust chunk size based on the specific use case and model limitations

Author: Ritesh Yadav