This article was originally published on AI Study Room . For the full version with working code examples and related articles, visit the original post. RAG Pipeline Optimization: Production Best Practices RAG Pipeline Optimization: Production Best Practices RAG Pipeline Optimization: Production Best Practices Retrieval-Augmented Generation (RAG) combines information retrieval with LLM generation. Production RAG requires careful optimization of every pipeline stage. Chunking Strategies Document chunking determines what information is retrieved. Fixed-size chunking with overlap is simple but can split semantic units. Semantic chunking uses NLP to find natural boundaries (sentence, paragraph, section boundaries). Optimal chunk size depends on your retrieval task. 256-512 tokens works well for factual Q&A.; Larger chunks (1000-2000 tokens) preserve context for summarization. Smaller chunks improve precision. Agentic chunking summarizes each chunk for improved retrieval relevance.…