Welcome to the first installment of our three-part series on "Practical AI: From Theory to Added Value". This series is meticulously crafted to guide you through the dynamic world of Artificial Intelligence, from foundational theories to practical applications that deliver real value. In this opening segment, "Basics of AI" we lay the groundwork by exploring essential AI concepts including models, machine learning, and the groundbreaking Large Language Models (LLMs). Our journey will continue through "Initiating an AI Project" and culminate in "Your LLM Project" each part building on the last to provide a comprehensive understanding of AI’s potential and how to harness it.

What is Artificial Intelligence

AI's ubiquity in modern discourse is undeniable. From enhancing business operations to revolutionizing healthcare, AI's applications are vast and varied. The primary objective of this essay is to demystify AI for a non-technical audience and to elucidate its multifaceted applications in the business realm. By exploring the foundational aspects of AI, we aim to foster a deeper understanding of its capabilities and limitations.

Defining Artificial Intelligence

Artificial Intelligence (AI) encompasses the ability of computers or machines to address complex problems and execute decisions, a definition that emerges from within a landscape marked by diverse and sometimes nebulous interpretations of 'intelligence'. Intelligence, in this broad spectrum, is conceptualized as the capacity to assimilate knowledge from experiences, adapt to new circumstances, and apply this acquired knowledge in manipulating one's environment or in abstract thought processes.

AI strives to emulate human cognitive functions in a computational format, undertaking tasks once considered exclusive to human intellect, such as natural language comprehension, pattern recognition, and the resolution of intricate challenges. This pursuit transcends the mere technological ambit, proposing a synthesis between algorithmic computation and the intricate aspects of human thought.

It´s about Making Informed Decisions

In practical terms, intelligence manifests through the ability to navigate the world and make informed decisions. For example, deciding to bring an umbrella when the sky looks overcast in anticipation of rain, attributing a surge in chocolate sales to the approach of Valentine's Day, or interpreting a friend's short replies in a chat as a sign of their preoccupation or distress. These everyday decisions exemplify the application of intelligence — the use of models or simplified representations of reality to predict outcomes and resolve dilemmas.

Such examples illuminate the essence of intelligence as not only the theoretical capacity to solve problems but also its practical application in daily scenarios. Here, AI's objective is to replicate this decision-making prowess in machines, enabling them to learn from and interact with the world in a manner akin to human intelligence. This operational understanding of AI underscores its relevance beyond theoretical discussions, spotlighting its practical utility in addressing real-world challenges.

While Artificial Intelligence (AI) aspires to automate and enhance decision-making across various domains, it becomes evident that the nuanced realm of human intuition often plays a pivotal role where AI reaches its limits. This intersection highlights the complementary relationship between AI's analytical prowess and the depth of human insight. In instances requiring empathy, moral judgment, and creative thought—attributes deeply ingrained in the human psyche—AI's algorithmic approach may fall short. Thus, recognizing the synergy between AI's capabilities and human intuition becomes crucial, advocating for a collaborative framework where each enhances the other, leveraging AI's efficiency and scalability alongside the irreplaceable value of human discernment.

AI vs Human Intuition

Human intuition plays a crucial role in decision-making across various aspects of life, often relying on a combination of experience, emotional intelligence, and subconscious reasoning. Here are more scenarios where intuitive human decision-making is likely to manifest:

  1. Emergency Response: In an emergency, such as witnessing an accident, the immediate decision to call for help or provide first aid is often driven by intuition, guided by the urgency of the situation and the instinct to assist.
  2. Social Interactions: Deciding whether to trust a stranger based on non-verbal cues like body language and tone of voice. Humans intuitively assess these cues to make quick judgments about someone's trustworthiness or intent.
  3. Investment Decisions: While investing, individuals often rely on their gut feeling or intuition about a company or the market, influenced by their understanding and experience, even when all data may not fully support the decision.
  4. Career Choices: Choosing a career path or a job offer often involves intuitive decision-making, where one weighs the tangible benefits against a subjective sense of fulfillment, growth potential, and work-life balance.
  5. Parenting Decisions: Parents frequently make intuitive decisions about their child’s well-being, such as recognizing subtle signs of distress or happiness, often before the child has communicated anything explicitly.
  6. Navigation and Wayfinding: In unfamiliar environments, people often rely on intuition to choose paths or directions, especially when maps or GPS are unavailable, using landmarks and a general sense of direction.
  7. Food Selection and Cooking: Deciding what ingredients to combine when cooking without a recipe is an intuitive process, based on an understanding of flavors and personal preferences, often leading to creative and spontaneous meals.
  8. Problem Solving in Teams: In collaborative settings, deciding when to lead, when to follow, and when to compromise often relies on intuitive judgment about group dynamics, individual strengths, and the collective goal.

These scenarios illustrate the breadth of contexts in which intuitive human decision-making is applied, showcasing our ability to process complex information, often subconsciously, to navigate our environment and interactions effectively.


True, AI has made remarkable strides in replicating and even surpassing human capabilities in specific domains. Still, several facets of decision-making underscore the limitations of AI, advocating for a nuanced understanding of the roles of human and machine intelligence.

  • Human intuition encapsulates a rapid, often subconscious process of drawing on past experiences, emotions, and learned patterns to make decisions.
  • Decisions influenced by emotional intelligence involve understanding one's own emotions and those of others, empathy, and the management of social relationships.
  • Ethical and moral considerations in decision-making involve complex reasoning that balances individual, societal, and universal principles.
  • Creative decisions, such as those in art, literature, and innovation, often stem from an intuitive sense of what feels right or what might evoke certain emotions and thoughts in an audience.
  • Many decisions are made in the face of uncertainty and incomplete information, where human beings rely on gut feelings or instincts.
  • Many situations may simply lack clear historical data or patterns on which AI decision-making systems typically depend. Humans can draw from a broader context, applying flexibility and adaptability to navigate through unpredictability in ways that AI cannot.

While AI offers substantial benefits in optimizing, predicting, and automating decision-making processes, the inherent complexities of human intuition, emotional intelligence, ethical considerations, creativity, and the unpredictability of life highlight the limitations of AI. These domains underscore the indispensable value of human insight in the decision-making landscape, suggesting a collaborative rather than substitutive role for AI in augmenting human capabilities.

Understanding AI Models

The Role of Models in AI

At the heart of AI's ability to perform these tasks is the concept of models. Models in AI serve as simplified representations or abstractions of the real world, allowing computers to interpret, analyze, and predict outcomes based on data. These models are crucial for understanding and navigating the complexities of the world around us.

There are two primary types of models that inform AI development:

  1. Principle-driven models: These models are constructed based on explicit rules or theories. They operate under a set of predefined instructions derived from our understanding of the world. For example, a principle-driven model might utilize the laws of physics to predict the motion of a pendulum.
  2. Data-driven models: In contrast, data-driven models learn from historical data. Through the application of algorithms, these models analyze vast datasets to identify patterns, which are then used to make predictions about future events or decisions. Such models are at the core of machine learning, a subset of AI that emphasizes the ability of machines to learn from and make predictions based on data.

Both types of models play a pivotal role in the development of AI technologies. Principle-driven models offer a foundation rooted in established knowledge, while data-driven models provide the flexibility and adaptability to learn from new information. Together, they embody the essence of AI's capacity to mimic human intelligence by learning from the past, understanding the present, and predicting the future.

Artificial Intelligence represents a significant leap forward in our quest to create machines that can think, learn, and adapt. By leveraging both principle-driven and data-driven models, AI has the potential to solve complex problems, enhance decision-making processes, and transform industries.

Machine Learning - The Core of AI

Machine Learning is a pivotal subset of Artificial Intelligence focused on empowering machines to learn from data, identify patterns, and subsequently make informed decisions with minimal human intervention. This paradigm shift towards autonomous learning signifies a departure from traditional programming, where every decision-making rule needs to be explicitly coded. Instead, ML leverages algorithms that learn from and make predictions on data, embodying the essence of 'learning by example.'

The Process of Machine Learning

The ML process begins with training data—this data comprises examples that the machine uses to learn. Each example within the training data is tagged with the correct answer, enabling the machine to discern patterns and relationships within the data.

Following the ingestion of training data, algorithmic processing comes into play. During this phase, ML algorithms analyze the training data, identifying patterns and learning from them. The choice of algorithm depends on the type of task at hand (e.g., classification, regression) and the nature of the input and output data.

The culmination of this process is model creation. The model, refined through cycles of training and validation, encapsulates the insights gleaned from the data. Once adequately trained, the model can make predictions or decisions when presented with new, unseen data, effectively demonstrating its ability to learn from examples.

Practical Applications of AI

The advent of Machine Learning and Artificial Intelligence heralds a new era of innovation and efficiency across multiple domains. By harnessing the power of data, we can unlock unparalleled opportunities for advancement, streamlining processes, and enhancing decision-making. As we continue to explore and expand the frontiers of AI, the potential for transformative impact on society and the global economy remains vast and largely untapped. The real-world implications of AI and ML are vast and varied, impacting numerous sectors from finance to healthcare.

Credit Decisioning

A prime example of AI's practical application is in credit decisioning. Traditional credit assessments involved manual evaluation, which was time-consuming and subject to human error. AI revolutionizes this process by employing models trained on vast datasets, including credit history, transaction data, and other relevant financial information. These models can accurately predict an applicant's creditworthiness, thereby streamlining the decision-making process, reducing defaults, and enabling faster, more reliable credit distribution.


In healthcare, AI tools are being developed to diagnose diseases with accuracy comparable to or, in some cases, surpassing that of human experts. By analyzing patterns in medical imaging data, AI models can assist in diagnosing conditions such as cancer at early stages, significantly improving patient outcomes. Furthermore, AI-driven predictive models are being used to recommend personalized treatment plans, taking into account an individual's unique health profile and the efficacy of various treatment options.


The retail sector benefits from AI through enhanced customer behavior prediction and inventory management. By analyzing purchasing patterns, social media interactions, and other consumer data, AI models can predict trends, tailor marketing efforts, and optimize stock levels, ensuring that supply meets demand.


In manufacturing, predictive maintenance powered by AI minimizes downtime and reduces operational costs. By monitoring equipment data in real-time, AI algorithms can predict failures before they occur, allowing for timely maintenance and avoiding costly unplanned outages.

Relating ML, Generative AI & Large Language Models

In the rapidly evolving field of artificial intelligence, Large Language Models (LLMs) have emerged as a groundbreaking force, redefining our interactions with technology. Understanding the relationship between Machine Learning (ML), Generative AI, and Large Language Models (LLMs) provides a clearer view of their interconnected roles in the field of artificial intelligence.

Here's how these concepts are interrelated:

Machine Learning (ML)

ML is a subset of artificial intelligence that focuses on building systems capable of learning from data, identifying patterns, and making decisions with minimal human intervention.

It serves as the foundational technology that enables both Generative AI and LLMs. ML algorithms learn from vast amounts of data to perform a wide range of tasks, from simple classifications to predicting outcomes.

Generative AI

Generative AI refers to a class of AI systems and algorithms designed to generate new content, including text, images, videos, and more, that resemble human-generated content.

It uses advanced ML techniques, including generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to produce new data instances.

Generative AI is an application of ML, where the learned patterns and insights from data are used not just for analysis but for the creation of entirely new content that did not previously exist.

Large Language Models (LLMs)

Large Language Models are not just a technological innovation; they are a testament to the boundless potential of AI to enhance and enrich our lives. The advent of LLMs marks a pivotal moment in the journey of natural language processing technology. These advanced models, epitomized by the likes of ChatGPT, are not merely iterations of traditional chatbots; they represent a quantum leap forward, offering interactions that are astonishingly human-like in their responsiveness and depth. With their ability to learn from and adapt to the vast landscape of human language, LLMs offer a glimpse into a future where AI can communicate with understanding and empathy, transcending the limitations of earlier models.

LLMs, such as OpenAI's GPT (Generative Pretrained Transformer) series, are a specific instance of Generative AI focusing on generating human-like text based on the input they receive.

LLMs are trained using machine learning techniques on vast datasets of text, learning to predict the next word in a sentence given all the previous words, thereby generating coherent and contextually relevant text.

They represent a pinnacle of current Generative AI capabilities, being used for a variety of applications, including chatbots, content creation, and even coding.


Including Transformers as a technology and clarifying the role of Natural Language Processing (NLP), we can further refine the relationship among Machine Learning (ML), Generative AI, Large Language Models (LLMs), and place Transformers and NLP within this framework.

Transformers represent a breakthrough in how models process sequences, such as sentences in language tasks, by using self-attention mechanisms to weigh the relevance of different words within a sentence or sequence, enhancing both the efficiency and effectiveness of models like LLMs.

Beyond LLMs, Transformer models have become the backbone for a wide range of NLP applications, driving forward the state-of-the-art in understanding and generating human language.

Natural Language Processing (NLP)

NLP is both a technology and a field of study at the intersection of computer science, artificial intelligence, and linguistics, concerned with the interactions between computers and human (natural) languages.

It encompasses a range of techniques and models, including ML and specifically Transformer models, for processing and analyzing large amounts of natural language data. NLP applications range from translation and sentiment analysis to chatbots and voice recognition systems.

NLP serves as an application area for ML, leveraging techniques from ML (including those advanced by Generative AI and Transformer architectures) to solve specific problems related to understanding and generating natural language.

Summary of Relationships

This progression from ML to Generative AI, with the critical integration of Transformer technology, culminating in the development of LLMs, encapsulates the trajectory of AI's evolution towards more sophisticated, efficient, and creative applications.

ML provides the foundational algorithms for AI, enabling systems to learn from data.

Generative AI represents a creative leap in AI's application, using ML to produce new content.

Transformers are a key technological advancement enabling more sophisticated NLP and LLM applications by handling sequential data more effectively.

LLMs are a specific application of Generative AI in the field of NLP, built on Transformer technology, showcasing the generation of coherent and contextually relevant text.

NLP sits at the convergence of these technologies, applying ML (through models like Transformers) to interpret and generate human language, demonstrating AI's capacity to interact with and understand human communication.

This expanded framework illustrates how foundational technologies and models in AI build upon each other to enable complex applications, like understanding and generating human language, showcasing the deep interconnectivity within the field of artificial intelligence.



In concluding Part 1, we've laid the groundwork for understanding the vast and dynamic field of artificial intelligence. From defining AI to demystifying LLMs, our goal is to ensure you have a solid foundation upon which to build your knowledge. As we progress to Part 2, "Starting an AI Project," we invite you to join us in exploring the practical steps and considerations for turning AI theory into tangible value. Armed with these basics, you're now better prepared to navigate the complexities and possibilities that AI offers.

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