Understanding How Large Language Models (LLMs) Work
Large Language Models (LLMs), such as GPT-4 and Gemini, have revolutionized the way we interact with technology, transforming tasks from content creation to customer support. But how do these powerful AI tools really operate?
How LLMs Process Prompts
When you enter a prompt into an LLM, the model doesn’t actually “think” like a human. Instead, it processes text statistically:
Tokenization: Your input text is broken into tokens, small units of words or sub-words, converting them into numerical representations.
Embedding: Tokens are mapped into a higher-order numerical representation that adds semantic meaning and syntactic relationships to measure contextual similarity.
Self-Attention: Transformers use self-attention mechanisms to weigh token relevance based on the surrounding context. Data can then be split into batches and run concurrently on multiple processors, speeding results and reducing the computational power required.
Prediction: LLMs predict the next token step-by-step, generating coherent text based on learned patterns.
This approach, detailed in resources like NVIDIA’s “Introduction to Large Language Models,” is fundamentally statistical rather than cognitive (An Introduction to LLMs | NVIDIA).
More Parameters Mean Better Understanding?
Increasing the number of parameters (e.g., GPT-4.5’s 100 trillion parameters) typically enhances an LLM’s performance. More parameters allow models to capture complex language patterns better, improving their predictive capabilities and adaptability.
However, more parameters don’t equate to human-like understanding. Research indicates that LLMs operate through advanced pattern recognition, not genuine comprehension. For example, models often produce plausible yet incorrect responses—known as “hallucinations”—highlighting their lack of true understanding (Do LLMs Understand Us? | MIT Press).
CNNs vs. Transformers in LLMs
While Convolutional Neural Networks (CNNs) excel in image processing due to their ability to capture local patterns, transformers dominate in language processing. Transformers, introduced in 2017’s seminal paper “Attention is All You Need,” excel because:
• They handle long-range text dependencies efficiently.
• They process tokens simultaneously, enabling effective parallelization.
This makes transformers uniquely suited for text-based tasks, outperforming CNNs, which struggle with capturing context over long text sequences (Transformer vs RNN and CNN | Medium).
Why LLMs Are Powerful and Unique
LLMs represent a significant leap over previous AI technologies due to their versatility. Unlike earlier models, they require minimal fine-tuning to perform diverse tasks, from language translation to coding assistance. Their adaptability stems from their transformer architecture and extensive pre-training on massive, diverse datasets.
This versatility is what makes LLMs powerful tools across industries, offering applications that were previously unimaginable, such as aiding drug discovery or creative content generation (What are Large Language Models? | Elastic).
Responsible Use of LLMs
Despite their capabilities, it’s crucial to remember that LLMs are statistical pattern-matching machines, not infallible sources of truth. They can produce biased, outdated, or incorrect information. For example, GPT 4.5 was released in February of 2025 but its knowledge cutoff – the date training data stopped being ingested – occurred in October 2024. Effective interaction requires users to verify outputs and apply critical thinking consistently (Guidelines for Prompting LLMs | Medium).
Final Thoughts
Understanding how LLMs function helps us use them more effectively, leveraging their strengths and compensating for their limitations. Recognizing their statistical nature rather than anthropomorphizing them as intelligent beings ensures responsible, productive interaction with these remarkable AI tools.
