Interview Q&A on Document Chunking Strategies for RAG Systems

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1. What is Character Text Chunking, and how does it work?

Answer: Character text chunking is a simple method of splitting a document into smaller parts based on character limits or predefined character separators like periods, commas, or newlines. The goal is to ensure that each chunk stays within a specified character or token limit (e.g., 1,024 characters) so that it can be processed efficiently by language models. This approach is ideal for use cases where text lacks a structured format, but the chunks may lose semantic context if split arbitrarily.
Example: Breaking a long paragraph into smaller chunks of 500 characters each.

Answer: Recursive character text chunking extends the concept of character chunking by applying a hierarchy of separators (e.g., sentences, paragraphs, sections) to split the text more intelligently. Smaller chunks are created initially and then merged into larger chunks while still respecting the token or character limit. This hierarchical approach helps retain more context compared to simple character chunking.
Use Case: Processing legal or scientific documents where logical divisions (e.g., sections and subsections) must be preserved.
Example: Splitting a legal contract first into sections (based on headings) and then paragraphs within each section.

Answer: Token-based text chunking uses language model-specific tokenizers (e.g., GPT-4’s tokenizer) to divide text into chunks that have a maximum token count (e.g., 1,024 tokens). This strategy accounts for the fact that tokens are not equivalent to characters but represent units of meaning (words, parts of words, or punctuation). Token-based chunking is particularly effective for models with token processing limits because it aligns directly with their input constraints.
Use Case: Preparing input for GPT models where token limits (e.g., 4,096 tokens) must be respected.
Example: A paragraph of 1,500 words might be chunked into three parts, each with approximately 500 tokens.

Answer: HTML/Markdown/JSON chunking is a structured approach that leverages tags, elements, or layout structures within the text to create meaningful chunks. This method ensures that the formatting and logical grouping of content (like headers, bullet points, or tables) are preserved during chunking. It’s especially useful for technical or web-based documents where structure is critical.
Use Case: Chunking a Markdown-formatted blog post, an HTML webpage, or a JSON file with structured data.
Example: Splitting a Markdown document into sections based on headings (#, ##, etc.) or breaking a JSON object into separate key-value pair chunks.

Answer: Semantic chunking uses the meaning of the text to guide the chunking process. Instead of splitting arbitrarily, it analyzes the text (often through embeddings or natural language processing techniques) to ensure that each chunk contains semantically coherent information. This prevents loss of context and ensures that related concepts remain grouped together.
Use Case: Splitting research papers or FAQs into semantically meaningful parts to aid retrieval systems.
Example: A technical article might be split into chunks that group related paragraphs about a specific topic or subtopic, identified by similarity in their embeddings.

Answer: Agentic chunking relies on a set of rules, workflows, and tools, often powered by a language model, to intelligently chunk documents into meaningful sections. This approach considers the structure and layout of a document, such as headings, sections, and subsections, and creates chunks that align with these logical divisions. It is more dynamic and customizable compared to other methods.
Use Case: Processing business reports, academic papers, or any document with well-defined sections.
Example: A project report is divided into Introduction, Methods, Results, and Conclusion, with each section chunked into smaller, logical parts.

Answer: Contextual chunking enhances standard chunking methods by summarizing the context of the entire document or adjacent chunks and appending these summaries to each chunk. This approach helps preserve the broader context, which may otherwise be lost when the document is split into smaller parts.
Use Case: Feeding chunks into a language model for answering questions where understanding the entire document’s context is important.
Example: A summary of a financial report’s overall findings is added to each chunk that discusses specific sections of the report, ensuring the model retains a high-level understanding.

Answer: Late chunking uses a long-context embedding model to process the document as a whole, embedding each token while considering the full document context. Chunks are then generated along with embeddings that preserve the relationships between the parts of the document. This ensures that the most context is retained and avoids arbitrary splits.
Use Case: Advanced applications like training embeddings for long documents or processing novels and large technical reports.
Example: Embedding a full-length book and generating contextually rich chunks for summarization or retrieval without losing cross-chapter references.