Job of tomorrow...or job of today? | Generative ai in Finance and Banking | Introduction to Generative ai Google | Generative ai use Cases | Turtles AI

That of "prompt engineer" may soon become the new "job of the century". Or at least of this part of the century, so fervent with initiatives related to large language models (LLM), NLP (natural language processing) models and other prompt-based AI software. Proof of this is the announcement by Anthropic, a company in which Google has recently invested heavily, which is looking for a prompt engineer with experience and problem-solving skills. The position carries a very attractive salary of between US$250,000 and US$300,000. Even if this is a totally new role, a "prompt engineer" in AI would likely be responsible for designing and creating effective prompts to be used in NLP models. This would involve understanding the task or problem that the model is being trained to solve, and creating appropriate prompts that help guide the model towards the desired outcome. A prompt engineer might also be responsible for training and fine-tuning language models using prompt-based learning or prompt-tuning techniques. This could involve selecting or creating suitable datasets, defining and refining the prompts used to train the model, and evaluating the model's performance on various tasks. In addition to NLP, a prompt engineer may also work in other areas of AI, such as text-to-image prompting (ala Midjourney, Stable Diffusion or Dall-E) and any other text-to-something, based on deep learning and machine learning. Some notable examples of tasks that a prompt engineer might work on could include developing a model to generate written descriptions of images or videos, or training a chatbot to provide helpful responses to customer inquiries based on the prompts given to it. In the near future, a prompt engineer could become a highly coveted and specialized role within the broader field of AI, requiring a deep understanding of NLP and related techniques, as well as strong skills in data analysis, programming, and problem-solving.