As they have been for decades, databases are a key component in various types of business applications. Nowadays, modern databases also include features that support the construction of applications based on artificial intelligence. The starting point of this paper is the questions of what are the typical use cases in which a database and generative artificial intelligence work together, what database features are used in these applications, and what other technologies the applications rely on. The results contribute to one of our practice-oriented research projects, in which we build an environment that supports AI experiments for participating companies. As input, we use open online learning materials provided by three database vendors. The databases we selected for review are all non-relational, representing three different genres, and are the most popular representatives of their genre. For each training program of the vendors, we will examine the structure and scope of the program, the courses on generative AI included in the program, and the use cases and technologies related to generative AI presented in them. Finally, we will prepare a summary of the use cases and technologies found.

As general-purpose LLMs become widely accessible, tailoring their outputs to domain-specific tasks remains challenging. This paper investigates how custom, pattern-guided datasets and fine-tuning can specialize LLM behavior. We run a four-round open-vs-closed comparison: (1) evaluate commercially hosted models with an open-source dataset; (2) repeat with a synthetic, pattern-aligned variant; (3) replicate both rounds with open-source LLMs; and (4) demonstrate a real-world case by fine-tuning an open model to answer WordPress technical support queries using a finalized, pattern-constrained dataset. Across rounds, fine-tuned open-source models achieved performance comparable to closed-source baselines while offering greater controllability and deployment flexibility. We detail dataset design, the role of response patterns in producing consistent outputs, and practical trade-offs observed during fine-tuning and real-world use. The findings indicate that with well-crafted custom datasets, open-source LLMs can be reliably specialized for targeted applications without reliance on proprietary APIs.

In this article, the ongoing research on collaborative prototype development between university and enterprises is presented. The study of project featured numerous pilot cases and prototypes, executed in collaboration with organizations to address real-world challenges. This article assesses the appropriateness of the Descriptive Model for Prototyping Process (DMPP) for research project applications. We delve into two primary facets: the synergy between universities and enterprises, and the potential for artifact reusability within the DMPP. The article presents various pilot cases from the KIEMI project, highlighting the DMPP’s role in each. Furthermore, the paper evaluates the model, sets forward the challenges faced, and, finally, discusses topics for future research.

The paper examines the Third Mission of universities from the point of view of company collaboration in the prototype development process. The paper presents an implementation of university-enterprise collaboration in prototype development described by means of process modeling notation. In this article, the focus is on modeling the software prototyping process in a research context. This research paper introduces prototype development in a university environment. The prototypes are made in collaboration with companies, which offered real-world use cases. The prototype development process is introduced by a modeling procedure with four example prototype cases. The research method used is an eight-step process modeling approach. The goal was to find instances of activity, artifact, resource, and role. The results of modeling are presented using textual and graphical notation. This paper describes the data elicitation, where the process knowledge is collected using stickers-on-the-wall technique, and the creation of the model is described. Finally, the shortcomings found in our existing practices and possibilities for improving our prototype development processes and practices are discussed.

In this article the focus is on software evolution, which is an important part of software engineering. In practice, software development does not stop when a system is delivered but continues throughout the lifetime of the system. After the system has been deployed, external pressure for change can generate new requirements for the existing software. This change aspect, which is a characteristic of software engineering, should be taken into consideration when developing and modeling new software systems. In this paper the theme was studied using experience gained from the piloting of a reference system developed in an earlier research project carried out by T ampere University of Technology. Software evaluation is examined from the point of view of system developers, administrators (maintenance), and end users based on a concrete long-term piloting period.