Generative AI vs. Large Language Models (LLMs): Decoding the Distinction

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Generative AI has taken the world by storm – transforming many fields from education to software development to marketing – and is expanding its influence to other fields. But anything that gains popularity there arise misconceptions surrounding it, and same is the case with generative AI.

Believers v Skeptics

There are some who believe in the power of AI to a level that they dismiss the need for human creativity and input entirely. On the other hand there are some that argue against the idea that AI is capable of producing anything of value.

If you are in either camp you are missing the reality, which lies somewhere in the middle. This blog will help you get to that point by educating you on generative AI and LLMs.

Everyone and anyone can use generative AI but if you understand how it works you will be able to get far greater value out of it.

This blog will explain in detail what generative AI is and what LLMs are.

What is Generative AI?

Generative AI refers to a class of artificial intelligence systems capable of creating various types of content, including written text, images, videos, and audio clips. These AI models generate new content based on patterns and data they’ve been trained on, allowing them to produce original and coherent outputs that can mimic human creativity.

How Does Generative AI Work?

Generative AI operates through a series of fundamental steps to produce responses to user queries:

  1. Input Processing: The AI receives an input, which could be a text prompt, image, or other data forms.
  2. Model Activation: The input activates the AI model, which has been pre-trained on vast datasets.
  3. Pattern Recognition: The model analyzes the input, recognizing patterns and context based on its training.
  4. Content Generation: Utilizing the recognized patterns, the AI generates new content that aligns with the input.
  5. Output Refinement: The generated content is refined to ensure coherence, relevance, and quality before being presented as the final output.

The Architecture of Generative AI

The architecture of generative AI models is composed of several functional layers that work together to process inputs and generate outputs. The key components include:

  1. Input Layer: This layer receives and processes the initial input data.
  2. Embedding Layer: Converts input data into numerical representations (embeddings) that the model can understand.
  3. Encoder Layer: Analyzes the input embeddings, capturing essential features and patterns.
  4. Decoder Layer: Uses the encoded information to generate new content that aligns with the patterns recognized by the encoder.
  5. Output Layer: Produces the final output in the desired format (text, image, etc.).

How is the Model Built – The Functional Layers

Building a generative AI model involves several stages, each contributing to the model’s ability to learn and adapt:

  1. Data Collection and Preprocessing: Amassing vast amounts of data relevant to the content type (text, images, etc.) and preprocessing it to ensure quality and consistency.
  2. Training: The model undergoes extensive training using the collected data. This involves feeding the data through the model repeatedly, allowing it to learn patterns and relationships within the data.
  3. Fine-Tuning: Post-training, the model is fine-tuned using specific datasets to enhance its performance on particular tasks or content types.
  4. Evaluation and Testing: The model is evaluated and tested to ensure it generates high-quality and relevant outputs. Adjustments are made based on performance metrics and user feedback.
  5. Deployment: Once refined and tested, the model is deployed for practical use, where it can generate content based on real-world inputs.

    For more information on building AI models visit – How to Build an AI Model – An Enterprise Perspective!

How Does the Model Learn and Adapt? ​

Generative AI models learn and adapt through a combination of various learning methods, leveraging each for different aspects of training and improvement:

Supervised Learning

The model is trained on labeled data, where it learns to associate specific inputs with the correct outputs.

A text generation model might learn to generate coherent sentences by being trained on pairs of sentences. For instance, given the sentence “The cat sat on the mat,” it might learn to generate a continuation like “and watched the birds outside.”


Backpropagation and gradient descent are commonly used to minimize the error between predicted and actual outputs during training.

Unsupervised Learning

The model identifies patterns and structures within unlabeled data, enhancing its ability to generate diverse content.

A model might be exposed to a large corpus of text without any labels and learn to generate realistic text by understanding the underlying structure of the language.


Clustering, dimensionality reduction (e.g., Principal Component Analysis), and autoencoders are often used to find patterns and structures in data.


The specific features and functionalities you want to include in your app significantly impact the cost. Here are examples of basic and advanced features along with their average development costs:

Reinforcement Learning

The model receives feedback on its outputs, allowing it to adjust and improve future generations based on this feedback.

A chatbot might improve its conversational responses based on user interactions and feedback, learning to provide more relevant and engaging replies over time.


Reward-based systems where the model is rewarded for good responses and penalized for poor ones, using algorithms like Q-learning and policy gradients.

Transfer Learning

The model leverages knowledge gained from previous training tasks to improve performance on new, related tasks.

A language model pre-trained on a vast corpus of general text can be fine-tuned on a smaller, domain-specific dataset (e.g., medical texts) to perform better in that specific area.


Fine-tuning pre-trained models on new datasets, often using techniques like freezing certain layers of the model while updating others.

Continuous Learning

The model continuously learns from new data and user interactions, adapting to changing patterns and requirements over time.

An AI system deployed in a customer service setting might continuously update its knowledge base with new types of queries and responses, improving its accuracy and relevance over time.


Online learning, incremental updates, and dynamic model adjustments to incorporate new data without requiring complete retraining from scratch.

Combination of Methods

Generative AI models typically use a combination of these methods to achieve high performance and adaptability:

  1. Initial Training: Often starts with supervised learning on large labeled datasets to establish a strong foundational understanding.
  2. Pattern Recognition: Unsupervised learning helps the model recognize deeper patterns and structures in data, which can improve its generative capabilities.
  3. Feedback Loop: Reinforcement learning fine-tunes the model based on feedback, making it more responsive and adaptive to specific tasks.
  4. Domain Adaptation: Transfer learning allows the model to be repurposed for specialized tasks without extensive retraining, saving time and computational resources.
  5. Continuous Improvement: Continuous learning ensures the model stays up-to-date with new information and adapts to changing environments and requirements.

What is LLM?

Large Language Model (LLM) refers to a type of artificial intelligence model that has been trained on vast amounts of text data to understand and generate human language. These models leverage deep learning techniques to process and generate text that is coherent and contextually relevant. Examples of LLMs include OpenAI’s GPT-4, Google’s BERT, and other similar models.

LLM’s Role in a Generative AI Model

In a generative AI model, the LLM is primarily responsible for:

Comprehending Input: Understanding the context and meaning of the input text provided by the user.

Generating Output: Producing text that is contextually relevant and grammatically correct based on the input.

Contextual Understanding: Maintaining context over longer conversations or text passages to provide coherent and relevant responses.

Language Translation: Translating text from one language to another while preserving meaning and context.

Summarization: Condensing large amounts of text into shorter, meaningful summaries.

Other Components in a Generative AI Model

Besides the LLM, a generative AI model typically comprises several other key components:

Preprocessing Unit:

Text Normalization: Converting text to a standard format, including lowercasing, removing punctuation, and correcting spelling errors.

Tokenization: Splitting text into smaller units called tokens, which are used as input for the LLM.

Embedding Layer:

Word Embeddings: Converting tokens into dense vectors that capture semantic meaning and relationships between words.

Attention Mechanism:

Self-Attention: Allowing the model to weigh the importance of different words in a sequence when generating a response.

Multi-Head Attention: Enhancing the model’s ability to focus on different parts of the input text simultaneously.


Language Generation: Generating coherent and contextually appropriate text based on the input and learned patterns.

Postprocessing Unit:

Text Formatting: Ensuring the generated text adheres to proper grammar, punctuation, and formatting rules.

Filtering: Removing inappropriate or irrelevant content from the generated output.

Feedback Loop:

User Interaction: Incorporating user feedback to refine and improve the model’s responses over time.

Reinforcement Learning: Using techniques to fine-tune the model based on user interactions and feedback.

Training Infrastructure:

Data Collection and Curation: Gathering and preparing large datasets for training the LLM.

Computing Resources: Leveraging powerful hardware (e.g., GPUs, TPUs) to train and fine-tune the model.

Integration Framework:

APIs and Interfaces: Providing mechanisms for other applications and systems to interact with the generative AI model.

Deployment Environment: Ensuring the model can be deployed efficiently and effectively in various settings, such as cloud or edge computing.

Examples of Generative AI with Large Language Models

Text Generation

OpenAI’s GPT-4

OpenAI’s GPT-4 is a state-of-the-art language model that excels in generating coherent and contextually relevant text. It assists writers in drafting content, brainstorming ideas, or overcoming writer’s block. Content creators use GPT-4 to produce initial drafts of blog posts, articles, and even books, allowing for faster content production and ideation.

Google Dialogflow

Google Dialogflow is a powerful platform for building conversational agents. It utilizes large language models to provide natural and engaging interactions. Businesses use Dialogflow to create customer support chatbots that handle inquiries, process returns, and provide product recommendations, enhancing customer service experiences.

Image and Art Generation


OpenAI’s DALL-E is a generative model designed to create unique images based on textual descriptions. It leverages a variant of large language models adapted for image generation. Artists and designers use DALL-E to explore new creative possibilities and generate artwork quickly. It can produce a series of surreal images for digital art exhibitions or custom illustrations for various projects.

Code Generation

GitHub Copilot

GitHub Copilot, powered by OpenAI’s Codex, is an AI tool that assists in automated coding. Codex is a descendant of the GPT-3 model, tailored specifically for coding tasks. It generates code snippets, completes functions, and can even write entire programs based on natural language descriptions. Software developers use GitHub Copilot to accelerate coding tasks, generate boilerplate code, and improve coding accuracy.


Generative AI has become one of those technologies that is providing value to every rung of the society from the largest of corporations to the common households. 

Being a technology so vastly utilized, its only natural to assume that not every user will be aware of how the technology operates. In fact, it’s quite reasonable to assume that vast majority of them will be unaware. 

For those who are unaware but curious, this blog explains how generative ai works and how it utilizes large language models to provide value in a variety of ways.

About the Author

Samar Ayub is an accomplished Project Manager with over 8 years of dedicated service in the field of Mobile and Web Applications development. Having overseen the development of more than 15 live applications, which are available on both the App Store and Play Store, Samar’s work has directly impacted the lives of over 500,000 users worldwide. With a keen focus on Product Discovery and MVP Development, Samar brings a wealth of expertise to every project she undertakes. She is available on LinkedIn for further discussion.

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