Chunking in RAG: why your splitter matters more than your embedding model
The article discusses the importance of chunking strategies in retrieval-augmented generation (RAG) systems. It emphasizes that the choice of chunker significantly impacts the effectiveness of embedding models. The author presents four common chunking strategies and critiques the effectiveness of semantic chunking compared to simpler methods.
- ▪The chunker determines what the embedding model can access, making it crucial for effective retrieval.
- ▪Semantic chunking often underperforms compared to fixed-size and recursive character splitting methods.
- ▪Research indicates that chunk size and overlap are more critical factors than the specific chunking strategy used.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3933168) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } saurabh naik Posted on May 18 Chunking in RAG: why your splitter matters more than your embedding model #ai #rag #llm #python Most RAG retrieval problems I've debugged came down to the same thing: someone swapped the embedding model three times, added a reranker, then gave up — and never once changed the chunker. This is backwards. The chunker decides what your embedding model is allowed to see. A great embedding on a bad chunk is still a bad retrieval.
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