AI engineering requires no academia or ML – just problem-solving

AI engineering doesn't require academia or machine learning expertise. Tejas Kumar, an AI DevRel Engineer at DataStax, emphasizes that it involves applying AI to solve problems, often through AI API requests. Key techniques include fine-tuning transfer learning and optimizing model architecture to reduce costs. To mitigate AI hallucinations, Kumar recommends Retrieval-Augmented Generation (RAG), and to ensure privacy, running models locally using tools like LLVM or llama.cpp. More insights will be shared at the Shift Conference in Zadar.