Prompt Engineering is an emerging field within Artificial Intelligence that focuses on the ccreating sequences of input tokens to guide language models in generating consistent text and relevant in response to a specific task or question. This technique has proven to be very useful in a wide variety of applications, from predictive text generation to natural language assisted programming.

The importance of Prompt Engineering lies in the need to control and guide the output of language models, since these have a natural tendency to produce irrelevant or even inappropriate text. By providing the model with a suitable prompt, we can influence the generated output, which can be especially useful for tasks where a specific answer is required or inappropriate text output needs to be avoided.

Prompt Engineering allows for greater flexibility and customization in text generation. By training the models with specific prompts, we can adjust the generated output to meet the needs of a specific task or application. This can significantly improve the quality of the generated text and make the task more efficient and effective.

The fact is that if you have tried Midjourney for a few days, you will know that you have to have a lot of practice to get the image we have in mind. With the right prompt, you can achieve almost anything.

The subject is so important that today we are launching a category on WWWhatsnew about Prompt Engineering, and every week we will send the most relevant in our newsletter.

A must read article

We kick things off with a must-read article, “Controllable Neural Text Generation: A Survey” by Lilian Weng, which explores how deep learning techniques can be used to generate text in a controlled manner. The goal of this approach is to allow users to influence the direction of text generation and ensure that more accurate and consistent results are produced.

In particular, the article focuses on the Prompt Engineering techniques, which involve adding a set of prefix tokens to a pre-trained language model to guide its text generation. These prefixes can be optimized through techniques such as AutoPrompt, Prefix-Tuning, P-tuning, and Prompt-Tuning, which seek to improve the quality and efficiency of text generation.

One of the main advantages of these techniques is that they can adapt to a wide variety of text generation tasks, such as machine translation, answering questions, generating summaries and creating creative texts. This is largely due to the flexibility of prefixes, which can be adjusted to emphasize certain aspects of the input context or to modify the behavior of the underlying language model.

The article also highlights the importance of external information retrieval for controlled text generation. Many text generation tasks require access to specific knowledge or data that is not available in the pre-trained language model. In these cases, information retrieval can be used to provide the model with access to external knowledge bases or to retrieve relevant information from the context.

Another interesting technique discussed in the article is the use of external APIs for text generation. Language models can be connected to external tools such as search engines or calculators to aid in text generation, allowing for a greater degree of precision and control in text generation.

Overall, the article highlights the importance of controlled text generation techniques for a wide variety of applications. As text generation becomes increasingly automated, these techniques can provide a means to ensure that results are accurate, consistent, and relevant to the input context.

These techniques can help improve the transparency and interpretability of language models, which is important in many practical applications. This is how we will manage the information in the future, do not forget it.