Why relying on retrieval-augmented generation and prompt engineering is preferable to investing in model training and fine-tuning.
The rapid development and improvement in generative AI technology pose a challenge for training and fine-tuning as a sustainable path for adoption. If organizations constantly need to fine-tune new models for specific tasks, they might be in a costly cycle of catching up with new technology. In contrast, prompt engineering and retrieval-augmented generation (RAG) focus on improving the retrieval and integration of information, a process that can continuously benefit from advances in generative technology. This is a more sustainable short-term adoption strategy.
[ This article is an excerpt from Generative Artificial Intelligence Revealed, by Rich Heimann and Clayton Pummill. Download your free ebook copy at the book’s website. ]
In a popular blog post titled “The Bitter Lesson,” Richard Sutton argues that general methods leveraging computation outperform specialized methods in AI research, fundamentally due to the decreasing computation cost over …