I went straight into fixed-size vs semantic chunking and talked about overlap windows, which felt solid at first.
Start by clarifying the use case and data types, then propose a chunking strategy that balances retrieval granularity and context preservation. Discuss trade-offs between chunk size, overlap, and metadata, and how to evaluate and iterate based on retrieval metrics.
Pro tip: Emphasize that chunking should be driven by the downstream task and retrieval evaluation; avoid one-size-fits-all and consider dynamic or hierarchical chunking for heterogeneous data.
Identify the types of documents (e.g., short messages, long articles, code) and the retrieval tasks (e.g., question answering, summarization). This informs the appropriate granularity and structure.
Select from fixed-size, semantic, recursive, or hierarchical chunking based on data characteristics. Consider using overlap to preserve context across boundaries.
Experiment with different sizes and overlaps, using retrieval metrics (e.g., recall@k, MRR) to find the sweet spot that balances precision and context.
Attach metadata (e.g., source, timestamp, section headers) to chunks to enable filtering and improve retrieval relevance.
Set up an evaluation pipeline with human or automated judgments to continuously refine the chunking strategy as data and queries evolve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.