When does LLM-based semantic chunking justify its cost, and how do I implement it efficiently?

LLM-based chunking is worth the cost only for high-value, semantically complex content where retrieval accuracy directly impacts revenue (legal discovery, medical literature, financial analysis). The method extracts "propositions" - self-contained semantic units - achieving 25-30% better context preservation than embedding methods. But it's 50-100x more expensive.

For production, implement a hybrid pipeline:

• use LLM chunking for your top 10% most-queried documents,
• embedding similarity for the middle 60%,
• and rule-based for the bottom 30%.

Optimization tips:

1. Batch documents and use smaller models (Mistral-7B vs GPT-4) for initial proposition extraction,
2. Cache proposition boundaries and only re-process when documents change,
3. Set proposition size limits (max_tokens=150) to prevent runaway costs.

Monitor proposition quality with a sample - if <80% are truly self-contained, your prompt needs tuning. Real-world benchmark: LLM chunking improved answer accuracy by 18% on multi-hop reasoning but increased indexing costs by 40x.