Atlas · GenAI 2026
Document Chunking
Semantic chunking (layout-aware, hierarchical)
conceptPeak: 2024Indexing & ChunkingAI consensus: 3/3
Prerequisites
Chunking only makes sense in the context of a RAG pipeline — it's the data preparation step that determines retrieval quality
- mediumNLP
Understanding token counts, embedding window sizes, and semantic boundaries requires NLP foundations
Recommended reference
Unstructured.io (2024) 'Chunking for RAG: Best Practices' — unstructured.io/blog; practical guide with benchmarks on chunking strategies
Notes from AI deep research
Anthropic Opus
Layout-aware > fixed-size. 80% jakosci RAG zalezy od tego jak pocialesz dokumenty
OpenAI Deep Research
Jakość retrieval i koszty [OA#30]
Google Deep Think
Świadomość układu strony, tabel, hierarchii [G#44]
Related skills
- → is part of: Retrieval-Augmented Generation(3/3)
- ← is subcategory of: Contextual Retrieval(0/3)