How poor chunking increases AI costs and weakens accuracy - LogRocket Blog

"As AI systems move from prototypes to production, teams quickly discover that rising costs and inconsistent accuracy are rarely caused by the model alone. Architecture, data preparation, retrieval design, and system constraints all shape how an AI feature behaves in real use. One of the most overlooked factors in this process is chunking, which refers to the way information is split before it's embedded and retrieved."

"Chunking is often treated as a minor preprocessing step, but it plays a central role in cost and accuracy. Poor chunking increases embedding and storage costs, reduces retrieval precision, and forces models to work with irrelevant or incomplete context. These issues show up in production environments as slower responses, higher infrastructure spend, and answers that feel inconsistent or unreliable to users."

"Even teams using advanced models and modern retrieval systems can struggle if their chunking approach is misaligned with their data and usage patterns. Teams that design chunking deliberately often achieve more accurate results at a lower cost while relying on simpler models and infrastructure. In many systems, chunking quietly determines whether an AI feature scales reliably or degrades under real-world conditions."

Chunking splits large text or structured data into smaller, coherent parts before encoding into vector embeddings. Chunks become the primary units used for retrieval during queries and workflows. Poor chunking increases embedding and storage costs, reduces retrieval precision, and forces models to operate with irrelevant or incomplete context. These effects manifest as slower responses, higher infrastructure spend, and inconsistent or unreliable answers in production. Misaligned chunking undermines even advanced models and retrieval systems. Deliberate chunking aligned with data and usage patterns produces more accurate results at lower cost and enables simpler models and infrastructure to scale reliably. Chunking should be treated as a core engineering and UX decision.

Read at LogRocket Blog