What is GenAI? An introduction to GenAI and its capabilities.

Artificial intelligence has become an inseparable element of the modern world. The media highlights its applications and development potential on a daily basis. For many companies, the challenge lies not only in keeping up with these innovations but also in selecting those solutions that best address their specific needs. So, how do we understand Generative AI and effectively approach its implementation to maximize its potential? That is precisely what we will explore in this article.

Basic concepts

To begin, it is crucial to understand the fundamentals of AI. We will start by explaining terms related to artificial intelligence to showcase its wide range of possibilities.

One commonly known term is artificial intelligence (AI). It refers to technology aimed at creating systems capable of emulating human activities, such as understanding language, making decisions, or solving problems, as well as attempting to replicate human intelligence in reasoning, learning, and adaptation.

A further development of AI is machine learning (ML) – a technique enabling AI systems to learn autonomously based on data. Rather than programming every possible action, ML analyzes data to build a predictive model. A good example is a system that learns to detect financial fraud by examining past transactions.

Another key concept is deep learning (DL), an advanced form of machine learning. It leverages multi-layered neural networks inspired by the workings of the human brain. Deep Learning is especially effective in processing complex, unstructured data such as images, audio or text. This technique allows AI systems to recognize images and convert speech to text.

Building upon Deep Learning, Generative Artificial Intelligence (GenAI) not only analyzes data but can also create entirely new content – text, images, music or even code. It is one of the newest, most innovative areas of AI and is deployed across various industries.

It is also worth clarifying that automation is a separate concept, though it is often interpreted as AI. Unlike AI and its advanced models, automation focuses on carrying out repetitive tasks according to predefined rules. Machines perform specified processes without the ability to improve autonomously.

Beyond these foundational technologies, there are several additional key concepts:

• Large Language Model (LLM)
  A model that utilizes billions of parameters to generate responses, enabling a deep understanding of linguistic context and nuances. An example is ChatGPT,
• Foundation Model (also referred to as a base model)
  A model that can be adapted for various applications due to its broad knowledge acquired during training on extensive datasets. Examples include Claude or Llama, which process both text and images, as well as stable diffusion, which generates images based on text prompts,
• Fine-tuning
  The process of tailoring an AI model using specialized data so that it can better address the needs of a specific industry or problem,
• RAG (Retrieval-Augmented Generation)
  A technology combining external or internal information retrieval with content generation, enabling AI to provide highly relevant and precise answers.

Our focus now turns to GenAI, which enables the creation of tools that support business operations by generating content, analyzing data, and making data-driven decisions based on patterns.

Legal regulations and risks in implementing GenAI

Generative AI has a significant impact on our personal and business lives. Ensuring appropriate safeguards is essential and adhering to guidelines that guarantee the highest level of security and responsible navigation in this space is key.

A valuable roadmap for adopting GenAI within an organization is the AWS Cloud Adoption Framework for AI (CAF-AI). These recommendations are based on the experiences of thousands of Amazon Web Services customers. The CAF outlines six key points:

• Clearly define goals and scope, including the departments and processes covered by the GenAI policy,
• Test each GenAI model and solution for compliance with internal standards and regulations,
• Develop a comprehensive risk management plan that incorporates GDPR and the AI Act,
• Train and engage employees to ensure safe usage of GenAI,
• Guarantee the ethical use of GenAI, including transparency toward customers and model impartiality, in line with the company’s values,
• Manage data in compliance with GDPR as a fundamental element of any GenAI strategy.

It is advisable to complement these foundational recommendations for implementing Generative AI with regulatory considerations. In Poland, the AI Working Group (GRAI) actively works on creating guidelines and support mechanisms for AI development in both the public and private sectors. At the European level, the key regulation is the EU AI Act — a comprehensive legislative framework coming into partial effect in 2025 and fully in 2027, classifying AI systems according to their level of risk.

Because of the topic’s importance, each of these aspects is thoroughly examined in a separate article, which we encourage you to read.

How to Implement GenAI in an Organization?

Implementing GenAI calls for a solid plan and strategy, clearly defining business objectives and the areas where GenAI can bring the greatest value. Start with an in-depth analysis of your organization’s needs, capabilities, and challenges, identifying specific GenAI applications. For example, automating processes or analyzing data. Next, ensure compliance with legal regulations and guidelines on ethics and security, especially regarding data protection. It is also essential to train your teams to use these new tools responsibly and efficiently.

A strong example of successfully designing and implementing a GenAI-based tool comes from one of our clients, the owner of a low-code platform for collaboration and remote work management. They faced a time-consuming and ineffective onboarding process for new employees. Our dedicated team created a GenAI-based Chatbot that answers questions from newly hired staff on a dedicated Slack channel. The solution is backed by internal data repositories and AWS services. As a result, the onboarding period was reduced from 3 months to 1.5 months, and costs decreased by 40%. We invite you to read the full case study to see exactly how we achieved these outcomes.

Your Challenges, Our Solutions

It is worth remembering that each organization’s journey toward implementing generative AI is unique, so solutions should be tailored to specific needs.

If you have an idea for using GenAI or you see issues in your organization that could be solved with AI, get in touch with us at kontakt@lcloud.pl.

We offer free consultations and will help plan your entry into the world of artificial intelligence.