Prompt Engineering: Is it really the next most promising skill for making the best use of Generative AI?

Prompt Engineering is the way we interact with computers to extract information and optimize the use of language models. It is a technique of applying the most appropriate words/text while searching results through chat GPT. It is a complex task or a process of designing and constructing effective prompts for getting the most out of language models. One can ensure accuracy and relevance of search results if one has the skill of prompt engineering. It is the new trend for IT solution engineers to utilize prompt engineering and get fast results for tasks like translation, code generation, debugging, and to reduce errors in the development, deployment, and management of IT solutions.

Prompt engineering focuses on crafting the optimal textual input by selecting the appropriate words, phrases, sentence structures, and punctuation. It has been declared by World Economic Forum as the number one “job of the future”. Companies ranging from digital advertising agencies to software developers, healthcare providers, and utility companies are advertising for prompt engineers and hiring them for the job.

There are courses offered by reputed educational institutes and universities to teach how to use prompt patterns to tap into powerful capabilities within large language models and to help create complex prompt-based applications for one’s life, business, or education. Generative artificial intelligence (AI) tools—including ChatGPT, Google’s Bard, Midjourney, Dall-E, and others require that users speak their language, and that language is natural-sounding, everyday English.

However, there are two opposing views available about the skill of prompt engineering.

A large group of companies and AI experts believe that to bring out the full potential of AI, prompt engineering is inescapable. Some jobs like AI prompt engineer, natural language processing engineer, machine learning engineer, and data scientists think that prompt engineering is required as part of its core skill set. People with these profiles act as a liaison between human intentions and understanding of machines. AI Prompt Engineers are responsible for crafting and refining the prompts or queries that users give as input to AI models. They possess a deep understanding of language nuances, context, and domain-specific vocabulary. This is where linguistics and data sciences are merging; they must make sure that the content generated by AI aligns with user expectations and industry standards.

Similarly, Natural Language Processing Engineers are linguistic architects as they can extract accurate and contextually relevant outputs making the best use of their insights and decision-making skills.  Machine Learning Engineer who specializes in prompt engineering also works alongside Prompt Engineers and NLP Engineers to refine prompting strategies and expectations. In addition, if a Data Scientist can add prompt engineering into their toolkit, they can find themselves as an effective AI communicator.

However, despite the hype being made about prompt engineering skills, some AI experts believe that this skill has only limited value and its significance will fade away with further developments in AI. First, future generations of AI systems will get more intuitive and highly skilled at understanding human intentions and natural language, and we may not even need engineered prompts.

Another reason they give is that the new AI language models like GPT4 already show great promise in crafting prompts — hence making prompt engineering already reaching its summation. Lastly, the effectiveness of prompts is dependent upon the specific algorithm, limiting their utility across diverse AI models and versions. It is said that Prompt engineering points to the limitations of NLP. Therefore, AI experts are trying to simplify the process of extracting knowledge so that results are not dependent on knowing how to write a decent query for individual AI tools is a major factor in getting the best results from and better training for the AI. They say that prompt engineering is important today because of the limitations of the current GPT architecture, but as the architecture evolves, the need for prompt engineering will lose its significance.

One needs to be able to express what you want the AI to do in a precise and clear way, just like if you were giving instructions or training to a human workforce. However, in future AI experts will make sure this language dependency is no longer a requirement.In a nutshell, prompt engineering is a dynamic and evolving field, and skilled prompt engineers play a crucial role in harnessing the capabilities of LLM AI models effectively. As of now, there are certain qualifications need for being a good prompt engineer like understanding LLM AI models, having domain knowledge, and staying updated with the latest advancements in prompt engineering, but as the field advances, many of the soft skills needed to get the best results from generative AI applications as opposed to hard, technical skills will diminish.