#chunking

Learn how to evaluate chunking effectiveness in RAG pipelines using metrics, Ragas, and real-world trade-offs from production systems.

Learn effective RAG chunking techniques for tabular data including CSV, Excel, and Parquet files, with production-tested strategies and code examples.

Sizes, overlap, and semantic boundaries. Get this wrong and nothing after it can save you.

Learn proven RAG chunking best practices for LLMs - optimal size, overlap, tabular data, evaluation, and tools - based on real production experience.

Most RAG failures are not model failures. The model did not forget how to read. The prompt is not...

Chunking is more than dividing content into cards. Learn to group meaning, preserve context, and test whether your interface helps people understand.

A five-question retrieval experiment shows where fixed-size chunking breaks, why overlap doesn’t always help, and when the real problem is retrieval rather than chunking.

How to split documents for retrieval-augmented generation: chunk sizes that retrieve precisely, overlap that preserves context, and structure-aware sp

Ask ten AI teams what they do and most will say context engineering. Two years ago they said prompt...

Chunking code by token count the same way you'd chunk prose breaks function boundaries and destroys context. A code-aware retrieval pipeline needs a d

Testing 128, 256, 512 and 1024-token chunks on the same retrieval pipeline, with the same eval set, to see which size actually moved recall and answer

Se você já mexeu com RAG (Retrieval-Augmented Generation), provavelmente já ouviu esses três termos...