Image by Editor | Midjourney & Canva
Introduction
Generative AI wasn’t something heard about a few years back, but it has quickly replaced deep learning as one of AI’s hottest buzzwords. It is a subdomain of AI — concretely machine learning and, even more specifically, deep learning — focused on building models capable of learning complex patterns in existing real-world data like text, images, etc., and generate new data instances with similar properties to existing ones, so that newly generated content often looks like real.
Generative AI has permeated every application domain and aspect of daily lives, literally, hence understanding a series of key terms surrounding it — some of which are often heard not only in tech discussions, but in industry and business talks as a whole — is key comprehending and staying atop of this massively popular AI topic.
In this article, we explore 10 generative AI concepts that are key to understanding, whether you are an engineer, user, or consumer of generative AI.
1. Foundation Model
Definition: A foundation model is a large AI model, typically a deep neural network, trained on massive and diverse datasets like internet text or image libraries. These models learn general patterns and representations, enabling them to be fine-tuned for numerous specific tasks without requiring the creation of new models from scratch. Examples include large language models, diffusion models for images, and multimodal models combining various data types.
Why it’s key: Foundation models are central to today’s generative AI boom. Their broad training grants them emergent abilities, making them powerful and adaptable for a variety of applications. This reduces the cost needed to create specialized tools, forming the backbone of modern AI systems from chatbots to image generators.
2. Large Language Model (LLM)
Definition: An LLM is a vast natural language processing (NLP) model, typically trained on terabytes of data (text documents) and defined by millions to billions of parameters, capable of addressing language understanding and generation tasks at unprecedented levels. They normally rely on a deep learning architecture called a transformer, whose so-called attention mechanism enables the model to weigh the relevance of different words in context and capture the interrelationship between words, thereby becoming the key behind the success of massive LLMs like ChatGPT.
Why it’s key: The most prominent AI applications today, like ChatGPT, Claude, and other generative tools, along with customized conversational assistants in myriad domains, are all based on LLMs. The capabilities of these models have surpassed those of more traditional NLP approaches, such as recurrent neural networks, in processing sequential text data.
3. Diffusion Model
Definition: Much like LLMs are the leading type of generative AI models for NLP tasks, diffusion models are the state-of-the-art approach for generating visual content like images and art. The principle behind diffusion models is to gradually add noise to an image and then learn to reverse this process through denoising. By doing so, the model learns highly intricate patterns, ultimately becoming capable of creating impressive images that often appear photorealistic.
Why it’s key: Diffusion models stand out in today’s generative AI landscape, with tools like DALL·E and Midjourney capable of producing high-quality, creative visuals from simple text prompts. They’ve become especially popular in business and creative industries for content generation, design, marketing, and more.
4. Prompt Engineering
Definition: Did you know the experience and outcomes of using LLM-based applications like ChatGPT heavily depend on your ability to ask for something you need the right way? The craftsmanship of acquiring and applying that ability is known as prompt engineering, and it entails designing, refining, and optimizing user inputs or prompts to guide the model toward desired outputs. Generally speaking, a good prompt should be clear, specific, and most importantly, goal-oriented.
Why it’s key: By getting familiar with key prompt engineering principles and guidelines, the chances of obtaining accurate, relevant, and useful responses are maximized. And just like any skill, all it takes is consistent practice to master it.
5. Retrieval Augmented Generation
Definition: Standalone LLMs are undeniably remarkable “AI titans” capable of addressing extremely complex tasks that just a few years ago were considered impossible, but they have a limitation: their reliance on static training data, which can quickly become outdated, and the risk of a problem known as hallucinations (discussed later). Retrieval augmented generation (RAG) systems arose to overcome these limitations and eliminate the need for constant (and very expensive) model retraining on new data by incorporating an external document base accessed via an information retrieval mechanism similar to those used in modern search engines, called the retriever module. As a result, the LLM in a RAG system generates responses that are more factually correct and grounded in up-to-date evidence.
Why it’s key: Thanks to RAG systems, modern LLM applications are easier to update, more context-aware, and capable of producing more reliable and trustworthy responses; hence, real-world LLM applications are rarely exempt from RAG mechanisms at present.
6. Hallucination
Definition: One of the most common problems suffered by LLMs, hallucinations occur when a model generates content that is not grounded in the training data or any factual source. In such circumstances, instead of providing accurate information, the model simply “decides to” generate content that at first glance sounds plausible but could be factually incorrect or even nonsensical. For example, if you ask an LLM about a historical event or person that doesn’t exist, and it provides a confident but false answer, that is a clear example of hallucination.
Why it’s key: Understanding hallucinations and why they happen is critical to knowing how to address them. Common strategies to reduce or manage model hallucinations include curated prompt engineering skills, applying post-processing filters to generated responses, and integrating RAG techniques to ground generated responses in real data.
7. Fine-tuning (vs. Pre-training)
Definition: Generative AI models like LLMs and diffusion models have large architectures defined by up to billions of trainable parameters, as discussed earlier. Training such models follows two main approaches. Model pre-training involves training the model from scratch on massive and diverse datasets, taking considerably longer and requiring vast amounts of computational resources. This is the approach used to create foundation models. Meanwhile, model fine-tuning is the process of taking a pre-trained model and exposing it to a smaller, more domain-specific dataset, during which only part of the model’s parameters are updated to specialize it for a particular task or context. Needless to say, this process is much more lightweight and efficient compared to full-model pre-training.
Why it’s key: Depending on the specific problem and data available, choosing between model pre-training and fine-tuning is a crucial decision. Understanding the strengths, limitations, and ideal use cases where each approach should be selected helps developers build more effective and efficient AI solutions.
8. Context Window (or Context Length)
Definition: Context is a very important part of user inputs to generative AI models, as it establishes the information to be considered by the model when generating a response. However, the context window or length must be carefully managed for several reasons. First, models have fixed context length limitations, which limit how much input they can process in one interaction. Second, a very short context may yield incomplete or irrelevant answers, whereas an overly detailed context can overwhelm the model or affect performance efficiency.
Why it’s key: Managing context length is a critical design decision when building advanced generative AI solutions such as RAG systems, where techniques like context/knowledge chunking, summarization, or hierarchical retrieval are utilized to manage long or complex contexts effectively.
9. AI Agent
Definition: While the notion of AI agents dates back decades, and autonomous agents and multi-agent systems have long been part of AI in scientific contexts, the rise of generative AI has renewed focus on these systems — recently referred to as “Agentic AI.” Agentic AI is one of generative AI’s biggest trends, as it pushes the boundaries from simple task execution to systems capable of planning, reasoning, and interacting autonomously with other tools or environments.
Why it’s key: The combination of AI agents and generative models has driven major advances in recent years, leading to achievements such as autonomous research assistants, task-solving bots, and multi-step process automation.
10. Multimodal AI
Definition: Multimodal AI systems are part of the latest generation of generative models. They integrate and process multiple types of data, such as text, images, audio, or video, both as input and in generating multiple output formats, thereby expanding the range of use cases and interactions they can support.
Why it’s key: Thanks to multimodal AI, it is now possible to describe an image, answer questions about a chart, generate a video from a prompt, and more — all in one unified system. In short, the overall user experience is dramatically enhanced.
Wrapping Up
This article unveiled, demystified, and underscored the significance of ten key concepts surrounding generative AI — arguably the biggest AI trend in recent years due to its impressive ability to solve problems and perform tasks that were once thought impossible. Being familiar with these concepts places you in an advantageous position to stay abreast of developments and effectively engage with the rapidly evolving AI landscape.
Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.
