Getting the most value out of every interaction with generative AI models. That, in a nutshell, is the definition of Prompt Engineering, a brand new discipline – it emerged alongside the ChatGPT phenomenon – that is gaining more and more importance within organizations.
An article in Business Insider, for example, noted that an expert in this area can earn high salaries.
In actual practice, Prompt Engineering is the ability to design in an optimal way the instructions or queries to be provided to an AI in order for it to produce the results desired by the user. It is a sort of real-time «configuration» of LLM models.
The objective? To find strategies, mechanisms and guidelines so that all content generated by the AI is relevant, coherent and consistent with business needs. This is a relevant topic. The consulting firm, Forrester, has already indicated that generative AI can augment and improve business processes in ways that were previously impossible.
Those who have tested models such as ChatGPT or other LLMs have already noticed this: it is not easy for AI to deliver exactly what someone expects to get.
Natural language is imprecise due to linguistic turns of phrase, irony, sarcasm or tones absent in the written text. The same sentence can change meaning depending on the context, the sender or the target.
Prompt Engineering, in this sense, consists precisely in making the AI fully understand the user’s intention, without ambiguities or significant deviations. Its importance lies in the fact that the language model interprets and generates text based on the instructions or queries presented to it. The better the prompt is formulated, the more accurate and appropriate the response will be.
Prompt Engineering: context and restrictions
It is believed that a well-designed prompt should include, in addition to the task specifications for the AI, the appropriate context for the model to understand what it must do and the restrictions and limitations for it to moderate its behavior (words it cannot use, type of responses it cannot give, etc.).
On the other hand, a good prompt engineer is the one who achieves a balance between clarity and precision of the instructions. Also, between the briefness and simplicity with which the prompts are issued. The more brief the prompts, the less likely it is that deviations will occur.
There is no need to fear mistakes: the process of trial and error, with multiple iterations until the perfect prompt is set, is part of learning.
The task of guiding and supervising the work of AI models is already having specialized departments. Today, there are prompt engineers who are experts in code generation, text creation or image design, to name a few examples.
Few shot: learning to learn
Among the concepts linked to the world of Prompt Engineering is the System message, which refers to the part of the initial text or prompt that is used to influence the behavior or response of the language model.
There is also the method known as few-shot learning. Within the prompt it is possible to provide the model with a series of examples of desirable questions and answers, to show the behavior that the user expects.
In that way, Prompt Engineering «teaches» the model to learn.
In conclusion, AI models have an unprecedented ability to combine huge amounts of data to produce the best answers for the business needs. Prompt Engineering, in this context, is positioned as the key to unlocking the main challenge for the future, which is asking the right questions.
Would you like to get the maximum value from your AI investments and get optimal use of the prompt? Our experts at Nubiral can help you. Schedule a meeting!
