Splitting documents by a fixed token count is a common and costly error in RAG pipelines. According to a cited study, switching to an adaptive, meaning-based chunking strategy (e.g., by paragraph) can increase fully accurate answers from 13% to 50%.
Vector similarity does not equal relevance. A lightweight re-ranker model, placed between retrieval and generation, rescores search results for usefulness. This significantly improves performance on ambiguous queries and is one of the highest-impact additions to a RAG pipeline.
Most production RAG systems fail not because of the LLM or prompt, but due to poor document parsing, chunking, and indexing. Teams mistakenly debug the generation layer when the foundational data processing is the true root cause of poor performance.
RAG and fine-tuning are not competing approaches but complementary tools. RAG provides a model with current, external knowledge, while fine-tuning shapes its style, format, and reasoning. The most robust AI systems combine both for optimal performance.