Most RAG systems fail at retrieval, not generation. Research from Anthropic and kapa.ai confirms the retrieval layer matters most. Chunking strategy, hybrid search, and proper evaluation determine whether your RAG system works in production or joins the 70% that fail.
The data quality problem that breaks AI is not imperfect data - it is how AI learns from your existing data problems and multiplies them until they destroy everything you built, with a RAND Corporation study linking the majority of AI project failures directly to data quality issues
Stanford and UC Berkeley research across twelve language models shows RAG vs fine-tuning is not about which is better. It is about data freshness, team capacity, and whether your knowledge changes daily or yearly.
Bad examples teach AI boundaries better than good ones. Testing hundreds of few-shot prompts in production at Tallyfy reveals why negative examples consistently improve AI performance by showing what not to do. The key is teaching systems what to avoid, not just what to do.