Chunking and Embeddings

Course overview

Chunking and embedding techniques: chunking strategies (fixed, semantic, recursive, document-aware), embedding model selection including multilingual options, deduplication and normalization, and incremental indexing with re-index triggers.

Compares chunking strategies suited to RAG pipelines and explains trade-offs by document structure.

Presents criteria for choosing embedding models and multilingual considerations for robust vector search.

Describes deduplication, normalization and content hashing methods to reduce redundancy and inconsistencies in indexes.

Explains incremental indexing and re-index triggers to keep data relevant without rebuilding the entire corpus.