Large Language Models: Principles, Examples, and Technical Foundations
Introduction
Large Language Models (LLMs) are Artificial Intelligence algorithms that use massive data sets to summarize, generate and reason about new content. LLMs use deep learning techniques capable of broad range Natural Language Processing (NLP) tasks. NLP tasks are those involving analysis, answering questions through text translation, classification, and generation [1][2].
Put simply, LLMs are computer programs that can interpret human input and complex data extremely well given large enough datasets to train and learn from. The generative AI technologies that have been enabled by LLMs have transformed how organizations serve their customers, how workers perform their jobs and how users perform daily tasks when searching for information and leveraging intelligent systems.
The core principles of LLMs
LLMs are built on a set of neural networks based on the transformer architecture. Each transformer consists of encoders and decoders that can understand text and the relationship between words and phrases in it. The transformer architecture relies on the next-word prediction principle which allows predicting the most probable next work based on a text prompt from the user. Transformers can process sequences in parallel which enables them to learn and train much faster [2][3][4]. This is due to their self-attention mechanism which enables transformers to process sequences and capture distant dependencies much more effectively than previous architectures.
The transformer architecture consists of three key components:
Embedding: To generate text using the transformer model, the input must be converted into a numerical format that can be understood by the model. This process involves three steps that involve 1) tokenizing the input and breaking it down into smaller, more manageable pieces 2) embedding the tokens in a matrix that allows the model to assign semantic meaning to each token 3) encoding the position of each token in the input prompt 4) final embedding by taking the sum of the tokens and positional encodings, and capturing the position of the tokens in the input sequence.
Transformer block: comprises of multi-head self-attention and a multi-layer Perceptron layer. Most models stack these blocks sequentially, allowing the token representations to evolve though layers of blocks which in turn allows the model to build an intricate understanding of each token.
Output probabilities: Once the input has been processed through the transformer blocks, it passes through the final layer that prepares it for token prediction. This step projects a final representation into a dimensional space where each token is assigned a likelihood of being the next word. A probability distribution is applied to determine the next token based on its likelihood, which in turn enables text generation.
Figure 1. Simple transformer block.
LLM applications
The transformer architecture allows LLMs to achieve a massive scale of billions of parameters. LLMs begin with pre-training on large datasets of text, grammar, facts and context. Once pre-trained, the models undergo fine-tuning where labeled datasets are used to adapt the models to specific tasks. The ability of LLMs to use billions of parameters, combined with their efficient attention mechanisms and their training capabilities, allows LLMs to power modern AI applications such as chatbots, content creation, code completion and translation.
Text generation: Chatbots and content creation
Text generation is one of the most prominent applications of LLMs where coherent and context-relevant text is automatically produced. This application of LLMs powers chatbots, like ChatGPT, that interact with users by answering questions, providing recommendations, generating images and conducting research [5].
GPT 4.5 and 4o feature multimodal capabilities allowing them to handle text and images for versatile use in different applications, and they can both handle text processing capacity of 25,000 words, though the amount of computational resources varies between the two models.
By leveraging their vast datasets, LLMs are also used for content creation such as social media posts, product descriptions and marketing. Tools like Copy.ai and Grammarly use LLMs to generate marketing copy, and assist with grammar and text editing. DeepL Translator uses LLMs trained on linguistic data for language translation.
Agents
Agentic LLMs refer to conversational programs such as chatbots and intelligent assistants that use transformer-based architectures and Recurrent Neural Networks (RNNs) to interpret user input, process sequential data such as text, and generate coherent, personalized responses [6]. Personalized responses to input text are achieved through context-awareness and analyzing conversations.
Agentic LLMs are also capable of managing complex workflows and can collaborate with other AI agents for better analysis. Vast datasets can be leveraged to support a variety of domains such as healthcare, finance and customer support.
Code completion
Code completion is a leading application of LLMs that uses the transformer-based architecture to generate and suggest code by predicting next tokens, statements or entire code blocks. In this context, transformer models are trained using self-attention mechanisms to enable code understanding and completion predictions [7]. The encoder-decoder transformer model is used such that the input is code surrounding the cursor (converted into tokens), and the output is a set of suggestions to complete the current or multiple lines.
Challenges and future directions
Large Language Models are still facing challenges related to ethical and privacy concerns, maintaining accuracy, avoiding bias, and managing high resource consumption [8].
Ethical concerns: LLMs are trained on massive datasets. There are still open questions as to who can use these datasets, and how and when they can be used. These datasets can also be biased and lead to biased output from LLMs, which can lead to misinformation and hate speech.
Data privacy: The use of massive datasets containing large amounts of user data poses significant privacy concerns. Safeguards in the use of data are required to train a model without compromising user privacy. As the use of LLMs becomes more mainstream, and as the size of datasets used to train them increases, so do the privacy concerns around their use.
Output bias: Existing biases in the available training data can cause LLMs to amplify those biases, leading to inaccurate and misleading results. This is particularly important for areas that require objective data analysis and output, such as law, healthcare and economics.
Hallucinations: LLMs are prone to “hallucinations” where the model output may seem reasonable, yet the information provided is incorrect. Hallucinations can be addressed through better training and validation methodologies to enhance the reliability of generated content.
Environmental impact: Training and deploying LLMs requires an extensive amount of energy resources, leading to increased carbon emissions. There is a need to develop more efficient algorithms while also investing in renewable and efficient energy generation that will lower the carbon footprint of LLMs, especially as their use and application accelerate.
Addressing these and other challenges such as regulatory compliance, security and cyber attacks will ensure that LLMs continue to use the correct input datasets while producing the correct output in an ethical, fair and unbiased manner. The integration of domain-specific knowledge through specialized fine tuning will also enable LLMs to produce more accurate and context-aware information that will maximize their benefits.
Conclusion
LLMs power a variety of applications, including chatbots to content creation, code completion and domain-specific automation. Using the transformer architecture and vast datasets to train and learn, they have emerged as a transformative discipline of artificial intelligence. LLMs have proved their outstanding capabilities in understanding, generating, and reasoning with natural language. While there are challenges to overcome for LLMs such as bias, accuracy, environmental impact, and domain specialization, it is expected that LLMs will become more efficient and trustworthy as algorithms improve and innovations are achieved through better fact-checking and human oversight.
References
[1] What are large language models (LLMs)
[2] What are large language models (LLMs)
[3] What is LLM (Large Language Model)?
[5] 10+ Large Language Model Examples & Benchmark 2025
[6] Chatbot Architecture: RNNs and Transformers
[7] ML-Enhanced Code Completion Improves Developer Productivity
[8] Raza, M., Jahangir, Z., Riaz, M.B. et al. Industrial applications of large language models. Sci Rep 15, 13755 (2025). https://doi.org/10.1038/s41598-025-98483-1
