Artificial Intelligence has led the world to a new revolution. This has especially been true after the introduction of tools like ChatGPT. At this time, Generative AI solutions and large language models (LLMs) have both been pivotal advancements.
The aim of these technologies is to streamline workflow, build innovative products, provide better user experience, and get customer insights. However, people tend to use these two technologies interchangeably.
Depending on the needs of the business, choosing one of the two is necessary. So, if you are wondering what the difference between the technologies is and which one will work best as per your needs, then keep reading.
What is Generative AI?
Generative AI is an essential part of artificial intelligence that is responsible for generating new and original content, including text, images, audio, code, or video. The ability that makes it so unique is that it can reuse what it knows to solve new problems.
How it works:
- Training: Gen AI models are trained on massive amounts of data, learning patterns, and relationships within the data.
- Generation: Once trained, the model can generate new content by sampling from the learned patterns.
- Adaptation: Gen AI can be adapted to specific tasks with minimal additional training.
Applications of Generative AI Solutions
Now that you understand what exactly generative AI is, let us look at the various solutions it can offer:
- Text Generation
- Image Generation
- Video Generation
- Audio Generation
- 3D Model Generation
- Data Synthesis
- Drug Discovery
- Material Design
Examples of Generative AI Solutions in Use Today
Synthesia, OpenAI Jukebox, and Midjourney are some of the various examples of generative AI solutions that you might be using currently.
What are Large Language Models?
Large Language Models or LLMs are a subset of generative AI solutions that specialized in processing and generating human-like tests. This technology is trained on massive amounts of text data, which allows them to learn the patterns and structures of language. This enables them to perform a variety of tasks.
What Tasks Can Large Language Models Perform?
- Generating text: LLMs can generate text in response to a prompt, such as writing articles, poems, scripts, or code.
- Translating languages: They can translate text from one language to another.
- Summarizing text: The technology can summarize long documents into shorter versions.
- Answering questions: They can answer questions based on the information they have been trained on.
Applications of LLMs
LLMs are becoming increasingly powerful and sophisticated, and they are being used in a variety of applications, such as:
- Customer service: LLMs can be used to power chatbots that can answer customer questions.
- Education: It can be used to create personalized learning experiences.
- Healthcare: LLMs can be used to analyze medical records and identify potential health risks.
Examples of Large Language Models
GPT-4, Llama 3.1, and Gemini are some of the examples of large language models currently in play.
It is important to note that as per Lopex research, nearly 67% of organizations using generative AI products that use LLMs to understand human language and produce content.
What is the Difference Between Generative AI and Large Language Models
| Factor |
Generative AI |
Large Language Models (LLMs) |
| Scope & Focus |
Creates content across formats like images, music, videos, and text. Focuses on creativity and diversity. |
Focused on understanding, processing, and generating human-like text for language-specific tasks. |
| Training Data |
Trained in diverse datasets including text, images, audio, and more. |
Trained on large text datasets from online/offline sources like websites, publications, and proprietary data. |
| Learning Process |
Uses patterns, structures, and styles from training data to generate creative content. |
Focuses on linguistic patterns, semantics, and grammar using transformer-based fine-tuning. |
| Functionality & Output |
Generates new, creative content (e.g., music, art, speech) with statistical alignment. |
Produces coherent, context-aware text for tasks like summarization, Q&A, and translation. |
| Core Technologies |
GANs, VAEs, and Transformers for data generation across media. |
Transformers, self-attention mechanisms, transfer learning, and tokenization for language understanding. |
| Applications |
Fraud detection, dynamic pricing, composing music, creating art, and more. |
Clinical documentation, customer support, text summarization, and translation. |
| Major Concerns |
Data privacy, copyright issues, workforce automation, quality control, and bias. |
Academic misuse, resource intensity, scalability, accuracy, misinterpretation, bias, and ethics. |
| Adaptability |
Highly versatile across industries and media types. |
Best suited for language-focused industries with relatively limited adaptability. |
From the definitions, it is clear that these two technologies vary greatly in their applications. Now, let us look at the various ways these two technologies are different on various factors:
Scope & Focus of the Technologies
Generative AI
Generative AI, as a technology, can create content in various formats, including images, music, video, and synthetic data. Generative AI solutions use learning patterns from the input data to create new content. Hence, it is more creative and diverse with every piece of content. In this, the generative content is also in alignment with real data, which makes it more viable.
Large Language Model
Large Language Models or LLMs are more focused on understanding, processing, and generating human-like text. These are focused on linguist patterns, semantic relationships, and overall context, ensuring responding well to prompts, and are good for tasks like translations, communication language processing, and more.
Training Data & Learning Process
Generative AI
Training of generative AI models requires a vast number of datasets in different types of media, like images, text, audio, and more. Generative AI services rely on this data to understand patterns, structures, and styles and use these to produce viable outputs.
Large Language Model
LLMs are trained on huge text datasets available in online and offline repositories. Online sources like websites, digital publications, and offline sources like licensed collections, proprietary data, and more are used to create LLMs that can understand the complexities of human language, like semantics, grammar, and more. Additionally, the use of a transformer-based model for fine-tuning data and
Functionality & Output
Generative AI
The main function of a generative AI service is to create content in different formats using advanced algorithms to create new output while keeping statistics intact. It works well in situations where you need to create fresh content every time, like composing music, art, or speech.
Large Language Model
For coherent and context-aware text, based on user prompts, you can use LLMs. These models leverage the attention mechanism to tasks like text summarization, question-answering, and more. It is ideal for quick responses, language interpretation, and more.
Core Technologies Used
Generative AI
- Generative Adversarial Networks (GANs):
GANs consist of two neural networks, a generator, and a discriminator, that work in tandem to create realistic synthetic data. The generator creates data while the discriminator evaluates it, forcing the generator to improve over time.
- Variational Autoencoders (VAEs):
VAEs are probabilistic models used to generate data by learning a compressed representation of input data. Unlike traditional autoencoders, they add randomness to the encoding process, enabling smoother data generation and interpolation.
Transformers are deep learning architectures designed to handle sequential data efficiently by focusing on relationships between elements in a sequence. Their self-attention mechanism allows them to weigh the importance of different input parts dynamically.
Large Language Models (LLMs):
Transformers are at the core of LLMs consisting of encoder and decoder, along with self-attention mechanisms. Transformers understand the language and relationship among words and phrases by fetching meaning from the sequence of text.
- Self-Attention Mechanism:
The self-attention mechanism allows models to evaluate and prioritize different parts of input data, enhancing context comprehension. It is fundamental to transformer architectures, enabling them to capture long-range dependencies effectively.
Transfer learning involves leveraging pre-trained models to solve new tasks, reducing the need for extensive data and training. Fine-tuning existing knowledge accelerates AI development and enhances accuracy.
Tokenization is the process of breaking text into smaller units, like words or subwords, for processing by AI models. It enables models to handle language systematically, converting input into a structured format for analysis.
Applications
Generative AI
Generative AI solutions have a range of applications, ranging from real-time fraud detection to dynamic pricing in e-Commerce, and much more.
Large Language Model
Every AI/ML development company also works on LLMs for clinical documentation, customer support, and other applications.
The Major Concerns
Generative AI
AI/ML services tools based on generative AI technology come with data privacy and copyright concerns, workforce automation concerns, and diligence towards quality and bias control.
Large Language Model
On the other hand, LLMs' challenges include academic concerns, resource intensity and scalability issues, accuracy, misinterpretation, bias, and other ethical concerns.
Adaptability
Generative AI
Generative AI is versatile across a vast number of media and industries, offering bespoke solutions for all requirements.
Large Language Model
LLMs work better for language-focused industries and, therefore, have limited adaptability when compared to generative AI. However, it still has a range of applications
Future of Generative AI and LLMs Going
In the near future, generative AI multimodal capabilities will only improve, allowing seamless integration of text, video, and image generation using one framework. It has the potential to change the gaming, film, and education industries dramatically.
Large language models will have better contextual accuracy, allowing them to tackle niche queries and offer reliable information. With fine-tuning in prompt engineering, it can reduce errors and improve applications in customer support of almost every industry.
Choosing the Right One – Generative AI and LLM
From the comparison, generative AI is ideal when you want to create a solution capable of giving out diverse types of outputs. At the same time, large language models are ideal for text-based, interactive applications. Businesses and tech professionals need to understand the difference between the two technologies to make informed decisions. Aligning the right technology for the right purpose will allow these organizations to create innovative AI-powered solutions.
If you want an organization that is up to date with the latest AI trends and is willing to create a solution that truly revolutionizes your operations, get in touch with MoogleLabs. We would be happy to help you build the best solutions, be it generative AI or LLM, to meet your needs.