Since tools like ChatGPT exploded into the public eye, there has been a growing curiosity around what makes them work. For those of us working in data analytics and education, these questions are more than curiosity. They are now central to how we think about skills, opportunity, and responsible innovation. Yet much of the conversation about AI is still wrapped in technical jargon. This blog aims to strip back the jargon as much as possible, and explain what the different cogs in the generative AI engine are.
Models, Training, and Adaptation
At the heart of every generative AI system is what’s called a foundation model. These are machine learning systems trained on vast amounts of digital content, including books, websites, computer code, images, audio and video. The idea is straightforward: by exposing the model to enough data, it learns patterns in language and meaning so that it can generate useful or realistic responses to new prompts.
The training process itself is complex. It requires huge computing power, specialised hardware, and expensive infrastructure. Most companies do not train models in-house. Instead, they rent capacity from cloud providers such as Amazon Web Services, Microsoft Azure, or Google Cloud, which offer powerful chips known as GPUs or TPUs. These chips allow models to process data in parallel, which dramatically reduces time and cost.
Once a model is trained, it is capable of a wide range of tasks but lacks specificity. To make it useful for particular applications, such as customer service or legal support, developers use techniques like fine-tuning or retrieval-augmented generation, often referred to as RAG. Fine-tuning involves retraining the model with more targeted data, which is resource-intensive but results in sharper performance. RAG works differently. It keeps the model the same, but gives it access to relevant documents during each interaction. You can think of it as offering a reference book alongside a question, allowing the model to give better answers without changing how it thinks.
Many of today’s models are also multimodal. That means they can process and respond to different types of input, such as images, audio or video, in addition to text. This opens the door to tools that can not only write emails but also analyse photos or interpret voice commands.
Infrastructure, Storage, and Orchestration
Once the model is ready, it needs a home, a brain and a workflow. That’s where the underlying tech stack comes into play. A key shift with generative AI is that we are no longer working with data in tidy tables. Instead, models operate using vectors, which are numerical representations of meaning. When you type a question, that question is turned into a vector and matched against a database of other vectors to find the most relevant information.
These vector databases, such as Pinecone or Weaviate, are optimised for fast, semantic search. Instead of looking for exact word matches, they look for meaning. This is why AI tools are often able to surface helpful content even when the phrasing is quite different.
To keep track of everything systems rely on orchestration frameworks. Tools like LangChain and LlamaIndex act like the conductor of an orchestra. They manage each step of the interaction, from pulling data to calling external tools and formatting a reply. This layer does not get much attention, but it is what enables AI to handle complex tasks in the real world.
All of this runs on containers and cloud environments. Tools like Docker make it easy to package up an AI application and deploy it consistently, whether on a single laptop or across a cloud cluster. Kubernetes helps manage these deployments, scaling them up or down depending on demand. This infrastructure means AI can run anywhere, not just in data centres but also on devices at the edge of the network
It is a fairly deep tech stack, and even in its most simplified state I can't avoid all jargon. But once you understand how data flows, input to vector, to retrieval and to generation - then it becomes easier to understand.
Explainability, Safety and Ethics
As these systems grow in capability, their social and ethical impact becomes harder to ignore. We cannot treat generative AI as just another tool. The outputs it produces affect decisions, influence opinions and in some cases, carry real risk. That is why a responsible AI stack now includes explainability, fairness, and governance.
Explainability refers to how well a model’s decisions can be understood. This is not easy. Many large models are black boxes, they provide answers, but we cannot easily see how those answers were generated. Tools like SHAP or LIME try to bridge that gap by analysing which inputs contributed most to a decision, giving users and developers a clearer view of the model’s behaviour.
Fairness is another key concern. Models trained on biased data will reflect those biases in their outputs. If a hiring tool has been trained on past recruitment patterns, it might reinforce inequalities that already exist. That is why bias audits, representative data and human oversight are critical. We must not assume that because AI is data-driven, it is neutral.
Governance includes regulation, documentation and internal policy. Across the world, different governments are taking different approaches. The EU AI Act is highly structured and includes specific requirements for high-risk systems. In the United States, approaches are more fragmented. Colorado’s AI Act is one example of a state taking the lead, requiring clear risk assessments, human review and user notifications. The UK’s ICO is positioning itself as flexible and supportive, offering guidance now with regulation potentially coming later.
At the Institute of Analytics, we believe that technical knowledge and ethical awareness must go hand in hand. That is why we offer training in both areas. Through our education platform and upcoming portfolio tools, we help professionals not only build AI projects, but also explain them clearly, show how risks were addressed, and provide evidence of ethical practice. The future of AI is not just about what it can do, but whether we can trust it and who gets to decide how it is used.
Conclusion
Understanding generative AI is no longer optional. It is becoming part of everyday systems, workflows, and decisions. But it does not have to be overwhelming. Once you see the stack, the models, the infrastructure, the governance, it is much easier to begin to understand how it all fits together.
