Introduction:

Generative AI is one of the most transformative technologies. Gen AI Systems are built using a modular architecture with multiple interconnected components.

As we know very well, GPT is the core engine behind many modern AI tools and assistants and one of the most advanced large language models (LLMs). And of course, there are many. Models trained on vast quantities of text data are capable of generating human-like language, code, images, and even music. Let's discuss this cutting-edge technology in detail in this article.

GPT (Generative Pre-trained Transformer)

GPT is a groundbreaking model architecture developed by OpenAI that has revolutionised how machines process and generate human-like language, text, images, audio, and video. As one of the most advanced large language models, this is the core engine behind many modern AI tools and assistants in current development.

At its base, GPT uses a transformer-based neural network that is pre-trained on massive datasets from the Internet. This pre-training process helps the model understand grammar, facts about reasoning patterns, and even nuances in tone. Once trained, it can be fine-tuned for specific tasks like Q&A model, content generation, summary extraction, code building, images, audio, videos and more.

Let’s discuss the Generative AI Architecture and its key Components

Generative AI systems are built using a modular architecture with various interconnected components. The following list of components is the main architectural building blocks:

Generative AI architectural building blocks

• Foundation Model Layer: This core transformer-based model is trained on massive datasets. It provides general-purpose language understanding and generation. The classic examples include: GPT-4, PaLM, LLaMA, Claude, Mistral
• Pretraining Layer: This layer involves training the model on large-scale structured, semi-structured and unstructured data. The model learns language patterns, grammar, world knowledge, and reasoning in this layer.
  • Large-scale learning phases using web-scale quantities (e.g., books, Wikipedia, GitHub, forums) or an internal dataset.
• Fine-Tuning & Instruction Tuning Layer: This layer adapts the foundation model's specific techniques, such as Supervised Fine-Tuning, RLHF, LoRA, and PEFT, to particular domains or tasks using labelled data. This aligns the model’s outputs with desired formats, ethics, and end-user business needs.
  • Supervised-Fine-Tuning (SFT): Using curated input-output pairs based on the dataset
  • Reinforcement Learning from Human Feedback (RLHF) aligns the output with human preferences
  • LoRA / PEFT / QLoRA - Efficient fine-tuning with fewer parameters to enhance the model performance.
  • Prompt Engineering Layer: We know that AI tools are only as effective as the prompts they are given. We design the input queries or prompts to guide the model’s response more effectively and efficiently. Ultimately, we must use zero-shot, few-shot, and chain-of-thought prompting. This is essential for tailoring the outputs without retraining the model.
  • Act as an interface where users input tasks/questions.
  • Prompts guide the model’s behaviour and its output.
  • Advanced techniques: role-based prompts
• Inference Engine Layer: The computational speed is an essential parameter for generating responses from a given prompt and the model, along with the reference data, either public or private. Eventually, the speed and efficiency are optimised using GPUs, TPUs, or accelerators. Supports deployment on cloud, edge, or hybrid systems.
  • Optimised for latency and efficiency with ONNX, TensorRT, HuggingFace Accelerate.
  • Run on the cloud, edge devices, or dedicated inference servers.
• Retrieval-Augmented Generation (RAG) Layer: Retrieves relevant documents or facts from knowledge bases or vector databases, which is an essential aspect in Gen AI, as it Improves accuracy and reduces hallucinations in generated responses.
  • It combines model output with information from vector databases, search indexes, etc.
  • Ultimately, it enhances the model’s accuracy and freshness by retrieving relevant external data/documents.
  • Tools: FAISS and Pinecone
• Orchestration Layer: Frameworks like LangChain or LlamaIndex are commonly used to coordinate interactions between components such as models, tools, prompts, and data sources. It manages the logic and sequence of complex AI-powered tasks.

  

  • Coordinates interaction between components and enables workflows
  • Frameworks: LangChain, LlamaIndex, Haystack, Semantic Kernel

What is an LLM?

A large language model (LLM) is an artificial intelligence model designed to understand, generate, and manipulate human language into text, summary, image, audio and video. It’s built using deep learning techniques, specifically the Transformer architecture, and trained on massive amounts of text data from the Internet, books, code, and more.

Key Features of LLMs:

• Language Understanding: It has a phenomenal capability to comprehend grammar, context, semantics, and tone.
• Text Generation: It can write essays, emails, code, poetry, and more based on the demand
• Multitasking: Performs translation, summarisation, question-answering, and conversation.
• Few-shot/Zero-shot Learning: It can perform tasks without explicit training by seeing a few examples in the prompt.

How LLMs Work: Large Language Models (LLMs) like GPT-4, Gemini, and LLaMA generate human-like text by leveraging deep learning techniques, massive datasets, and sophisticated architectures. Let’s discuss this now.

Core Architecture: The Transformer

LLMS are built on the Transformer architecture. The following are the key components and their functionality.

Self-Attention Mechanism: In simple terms, the purpose of the Self-Attention mechanism is to understand relationships between words in a sentence. It converts each word into a vector, also called an embedding. Here, the model computes attention scores to weigh the importance of other words. As an example: In "The cat sat on the mat", "sat" pays more attention to "cat" and "mat".

Multi-Head Attention: This expands self-attention by running multiple attention layers in parallel and captures different linguistic relationships in the given input, such as grammar, meaning, and context.

Feedforward Neural Networks (FFN): In the LLM architecture, Feedforward Neural Networks (FFNs) are crucial components that follow self-attention layers within each transformer block, as shown in Figure 2 below. Generally, they apply a two-layer dense neural network with a non-linear activation (like Relu or GELU) to each token independently, which helps transform and enrich the token embeddings with deeper semantic meaning. This contributes to the expressive power of the transformer.

Layer Normalisation & Residual Connections: In LLM architecture, Layer Normalisation standardises the inputs within each layer to stabilise and speed up training. It also ensures consistent scaling of activations, which helps prevent issues like exploding or vanishing gradients, as shown in Figure 2 below. Residual Connections skip over layers by adding the input back to the output of a layer. As shown in Figure 2, this enables deeper models to be trained effectively.

Together, they enhance convergence, maintain information flow, and improve the overall performance of transformers.

Training Process (Two Phases)

In this architecture, the training process will be carried out pre- and post-training (fine-tuning). Let’s discuss that process.

Pre-Training (Unsupervised Learning): This is the initial phase in which the model learns from an unlabelled text dataset, which can be small, medium, or larger. At this stage, the model is trained to predict the following words, technically called “Tokens”. This will help to fill in missing words and reasoning patterns, capturing the grammar and facts. It forms the foundational knowledge and lays the base for later fine-tuning and a real-world applications perspective.

It also builds a general understanding of language without needing to be task-specific. The main goal is to learn general language patterns from vast text data. There are a few leading methods for accomplishing this task, such as masked language modelling (BERT-style) to predict missing words in sentences and Next-Token Prediction (GPT-style) to predict the next word in a sequence.

• Example: Input: "The [MASK] sat on the mat." → Model predicts "cat." (BERT-style)
• Example: Input: "The cat sat on the" → Model predicts "mat." (GPT-style)

 

Fine-Tuning (Supervised Learning & RLHF): In the LLM architecture, fine-tuning is used for specific tasks, specifically for labelled datasets in terms of supervised learning. It improves performance on tasks like question answering, summarisation, etc. Following this, RLHF (reinforcement learning from human feedback) is applied further to align the model with human preferences and ethical standards.

Both tuning processes make the model more accurate, helpful, and safe for real-world use, aligning with ethical aspects.

• Used in ChatGPT, Claude, Gemini.

Inference Layer

The Inference Layer generates responses based on user input and the model’s learned knowledge in the LLM architecture. It uses decoding strategies to predict the following tokens. This layer ensures outputs are contextually relevant, confident, and clear for the user's question in general. We can understand how LLMs generate text using this layer when you ask a question. Internally, four steps are executed in this layer, as follows.

• Step 1: Tokenisation: In this step, the input text is split into tokens , which are generic sub-words, e.g., "unhappiness" → "un", "happiness." Each token is mapped to an embedding (numerical vector using a vector database).
• Step 2: Context Processing: In this step, the transformer processes the tokens through multiple layers of attention and FFNs and builds a contextual understanding of the input.
• Step 3: Decoding (Text Generation): The model starts by probabilistically predicting the next token, a word or sub-word. The common strategy applied here is Greedy Search, which picks the most likely next word; this is fast but repetitive. Beam Search keeps multiple likely sequences; this is better but slower than greedy search. Another strategy is Sampling (Top-k / top-p), which randomly selects from likely words, which might create diverse outputs.
  • Temperature Control: The adjustment randomness for low is deterministic, and for high is very creative, sometimes it might lead to hallucinations
• Step 4: Iterative Generation: This step repeats until a stop token is generated and a max length is reached.

Limitations & Challenges

Every architecture has limitations and challenges, including LLM's architecture. Let’s review them.

• Hallucinations: LLMs can generate confident text or summaries based on the input request and the architecture layer’s process, but they can also generate factually incorrect or exaggerated information, which can lead to unexpected imagination. This affects reliability in sensitive domains like healthcare or law.
• Bias and Fairness: In some scenarios, the models may reflect or amplify social, cultural, and political biases present in the training data. This can lead to toxic, unfair, or discriminatory outputs in industry and society, so we must be careful when training the model.
• Lack of Explainability: The outcomes are sometimes unexplainable since LLMs operate as black boxes, making it difficult to trace how they arrive at a decision or output. The AI product development industry needs accountability. However, we still have techniques to trace them using Explainable AI (XAI).
• Data Privacy & Security: Since a vast amount of data is being utilised for these technologies, we must have data privacy and security layers to align with country-specific regulations such as GDPR, PII, etc., to protect public data from accidental exposure or copyrighted content. We must also restrict the leakage of inputs and outputs and prevent unnecessary issues and threats to sensitive information if not carefully managed.
• Resource Intensive: Of course, to train the model, we need storage to store the massive volume of data, and for processing, we may require high computational speed. This limits accessibility for smaller organisations or on-device applications.
• Context Limitations: The context limitation is another roadblock to expanding our model beyond some limitations. Most LLMs have a context window limit, such as 4K to 128K tokens, restricting how much they can "remember" in a single interaction. As a result, the model may not perform as expected, and these limitations can lead to truncated or misinterpreted results.
• Real-Time Updating Challenges: The drawback of LLMs is that they can't learn new information dynamically. This means live match scores can happen in real-time live auctions and trading. Even if retraining or external tools like RAG are used, the real-time status would change then and there. Eventually, it will have the last training cut-off.
• Multimodal Limitations (for some models): Multimodal techniques are admirable, and industries look forward to them for various use cases. Unfortunately, not all LLMs can natively process images, video, or audio. GPT-4o, Gemini 1.5, Claude 3 Opus, and Kosmos-1 are classic examples of multimodal LLMs. These models combine vision, language, and sometimes audio understanding within a unified architecture, and they are useful in applications like visual Q&A, document intelligence, and AI tutors.

LLMs & Their Innovations

So far, we have discussed the core architecture, limitations, and challenges. We now understand that LLMS are a class of generative AI models trained on vast amounts of text data to understand and help us generate human-like language in terms of text, summary, image, audio and video. They are primarily based on Transformer architectures and have revolutionised natural language processing (NLP). Let’s focus on its innovation milestones.

Transformer Architecture(TA) (2017): Introduced in 2017, Transformer Architecture is a neural network model that revolutionised NLP by replacing RNNs and CNNs, which are, as we know, a bit more complex than other models. This TA uses self-attention mechanisms to weigh the importance of different words/tokens in a sequence format, enabling parallel processing.

It forms the foundation of modern LLMs such as GPT, BERT, T5, and more.

Innovation Highlights:

• It introduced self-attention as a mechanism to simultaneously focus on different parts of a sequence.
• It replaced RNNs and CNNs with a fully parallelisable architecture, significantly improving training speed over earlier models.

 

Generative Pre-trained Transformers (2018–Present GPT Series): This innovation and generation evolution for GPT is speedy and widely accepted. It adopts the simple approach of pretraining on large corpora and fine-tuning tasks.

Model Evolution:

• GPT-1 (2018): Proof-of-concept for unsupervised pre-training and supervised fine-tuning.
• GPT-2 (2019): 1.5B parameters, shocked the world with fluent long-form text generation and introduced zero-shot and few-shot prompting to handle the user inputs and perform better.
• GPT-3 (2020): The capacity has been increased to 175B parameters; additionally, it is capable of reasoning and creative writing. API-based access (OpenAI) has been enabled for users.
• GPT-4 (2023): This version brings Multimodal, better reasoning, and uses RLHF to align with human intention and support image, audio and video recognition. Supports images and possibly audio.
• GPT-4o (2024): This is a more advanced and specialised model, and it adds real-time audio, vision, and emotional response, creating human-like interaction and many AI tool developments. This has been added and is benefiting from it.

 

The rest of the models are also popular, but GPT grabbed the most market due to their capabilities. The other models are BERT & Bidirectional Learning (2018), Reinforcement Learning from Human Feedback (RLHF), Mixture of Experts (MoE), Retrieval-Augmented Generation (RAG) and Multimodal LLMs.

 

Innovation	Key Innovations	Impact / Examples
Transformer Architecture	Self-attention mechanism replacing RNNs/CNNs; enables parallel processing.	Foundation of GPT, BERT, T5, etc.
GPT Series	Massive scale pretraining + fine-tuning; few-shot and zero-shot prompting.	GPT-1, GPT-2, GPT-3, GPT-4, GPT-4o
BERT & Bidirectional Learning	Masked Language Modelling; bidirectional understanding of context.	BERT and RoBERTa widely used in NLP pipelines.
Reinforcement Learning from Human Feedback (RLHF)	Fine-tunes models using human feedback for better alignment and safety.	ChatGPT, Claude, Gemini – trusted conversational AI.
Mixture of Experts (MoE)	Activates a subset of model experts per input to improve efficiency and scalability.	Switch Transformer, Mixtral, GShard, GPT-4 (rumored).
Retrieval-Augmented Generation (RAG)	Combines LLMs with external information retrieval for grounded and factual outputs.	Perplexity AI, ChatGPT with Browsing, LangChain, LlamaIndex.
Multimodal LLMs	Processes multiple modalities such as text, image, audio, and video.	GPT-4V, Gemini 1.5, Claude 3 Opus, Kosmos-1.

Cutting-Edge Innovations in Gen AI & LLMs Use cases

Multimodal Capabilities:

These specific capabilities in Generative AI and LLMs have initiated advanced use cases. In which AI-powered tools act like humans and interact across text, image, audio, and video. One major use case is Visual Q&A, where users can upload an image, such as a medical scan image or a report and ask questions about it, with models like GPT-4o and Gemini providing perfect insightful responses based on the user.

Another common application is Image-To-Text Generation, which enables captioning and describes the uploaded images. This would be useful for understanding the nature of the image, publishing posts for sales and marketing, e-commerce product listings, and accessibility features for readers.

In enterprises, multimodal models are revolutionising Document Intelligence. They allow users to upload PDFs, scanned documents, and images and extract summaries, insights, and Q&A patterns to understand quickly and without spending huge amounts of time.

Since these models also support voice and video interaction, they enable real-time transcription, spoken question answering, and even multimedia content analysis for learning purposes.

In education, Multimodal Tutoring systems allow students to input any subject's problem statement as an image, audio, or voice and receive step-by-step guidance using proper annotations and verbal explanations.

With Assistive Technology for the Visually Impaired, students convert visual scenes into narrated audio, which helps users understand surroundings, read signs, or interpret documents.

Agents and Autonomous AI Systems: In fintech, LLM agents handle the entire life cycle of invoice processing, such as fetching emails, extracting data, verifying records, and updating ERP systems.

Code Generation and Autonomous Development: A startup can use this cutting-edge technology to convert project/product requirements into working Python microservices, reducing development time by 40% with effective code and best practices. It can auto-complete code, convert plain English to functional code, and suggest bug fixes. Many tools are on the market, including GitHub Copilot, Replit Ghostwriter, and Amazon's CodeWhisperer.

Other use cases include Personalised AI Companions and Assistants, AI-powered design and creativity Tools, Healthcare and Drug Discovery, and Generative Business Intelligence.

The Future of Gen AI & LLMs

The future of Generative AI and LLMs lies in more real-time, multimodal, and personalised interactions across all industries. These models will become faster, smaller, and wiser, running efficiently even on edge devices. They'll collaborate with tools and agents, allowing autonomous decision-making in workflows with more substantial alignment, ethics, and explainability. Indeed, Gen AI will evolve into a trusted intellectual assistant in day-to-day life.

• Specialised LLMs for healthcare, finance, law, and education
• On-device inference with smaller models (eg, edge AI)
• Human + AI collaboration models are becoming the norm in real-time use cases

Conclusion

We have discussed how Generative AI and Large Language Models (LLMs) have reshaped the landscape of artificial intelligence, redefining how humans interact with machines and how machines understand language, vision, and context perspective.

We started by introducing the Transformer architecture to the evolution of GPT, BERT, and beyond. We have observed an exponential leap in AI capabilities, driven by innovations like self-attention, multimodal integration, RLHF, and RAG.

We explored how technologies unlocked various applications, from code generation and content creation to document understanding, healthcare insights, visual Q&A, and autonomous decision-making in enterprise workflows. We observed how Multimodal performance and its intelligent capabilities.

Additionally, we learned that the challenges and limitations of powerful technology, such as bias, hallucination, data privacy, explainability, and computational intensity, remain critical concerns. As we deploy these systems across sensitive domains such as law, education, and healthcare, and understand how it is essential to ensure that models are not only intelligent but also aligned, ethical, and accountable.