With Figma, the task of designing websites and/or applications has been greatly simplified. In just a few clicks, excellent results can be obtained immediately ready to present to stakeholders and/or developers. However, Figma is not the only tool that can be used! We will find out what other tools can be integrated into the work of designing user interfaces.
The definition and development of user interfaces that are attractive, engaging, but above all easy and intuitive is a fundamental step in any application or website. There are a few basic rules that one must consider, although our personal aesthetic and creative taste will then influence our choices. By following the Formula of Elegance, however, fewer mistakes will be made and the final product will be as intuitive and inclusive as possible.
Developing web and desktop applications requires knowledge of several programming languages. Defining user interfaces is a fundamental aspect of providing a good user experience (UX) and viewing information in an effective and appealing manner. For those involved in analysing data or collecting it, however, this aspect is critical. In fact, many developers specialise in one language and are reluctant to expand their knowledge to other languages or tasks that are not their core business. For this reason, in this article we present five libraries in python that can facilitate and speed up the development of user interfaces.
Artificial intelligence has been gaining enormous interest in recent times. The application of deep learning and AI models to real scenarios has opened up new horizons. To generate models for our applications, however, we need data on which to train these models. Let us explore some ideas that could enable us to create new applications and services.
Textual search engines are a feature that plays an important role in the construction of applications. The user experience is greatly improved if the results requested are truly inherent to the words entered in the search bar. Elasticsearch allows us to integrate a full-text search system and obtain excellent results efficiently. Let us find out how and when it is possible to exploit these features.
The techniques proposed by prompt engineering allow even very complex tasks to be performed. Some of them, such as zero-shoot, few-shoot and Chain-of-Thought (CoT), manage to provide excellent results in some contexts. Where models have limitations, however, they can be remedied by even more advanced techniques such as Self-Consistency, Generated Knowledge and Tree of Thoughts.
Prompt engineering makes it possible to optimize the results obtained by acting only on what is provided to the LLMs. In recent years, several techniques have been proposed to appropriately structure prompts to solve even complex tasks without having to retrain the initial model. We will look at some simple techniques such as zero-shoot, few-shoot and Chain-of-Thought (CoT).
Creating prompts for LLM models, such as ChatGPT, can be considered a new art. From structuring prompts to using context, it is possible to improve the conversational experience and extract maximum value from interactions with LLMs. Through practical examples, we will see how to best structure prompts to generate engaging and meaningful conversations with AI based on the set task.
The advent of ChatGPT and LLM (Large Language Models) has revolutionized the world. Almost every industry has undergone a revolution and is adopting these powerful means of artificial intelligence to build new tools and services. But how do we build the prompts, i.e., the instructions, to make these models generate what we are interested in? Prompt engineering is the discipline that addresses this very issue. Let’s discover some tips for improving our prompts to artificial intelligence tools.
Databases can play an active role in validating and implementing business rules. Through triggers it is possible, in fact. to define rules to ensure data integrity and automation of critical operations. We will analyze their definition and the different types of triggers, focusing our attention on what is provided in Oracle. Through some practical examples, we will understand how to validate data and implement business rules without having to leave these aspects to applications.