LLMs Decoded: Architecture, Training, and How Large Language Models Really Work

Large Language Models (LLMs) like GPT-4o, Mistral, Claude, and Gemini have transformed how we interact with AI. But what’s really happening inside these models? How do they generate coherent text, code, and even images?

This deep-dive article breaks down how LLMs are designed, trained, and how they actually work — without overwhelming you with jargon.

🧠 What is an LLM?

A Large Language Model is an AI system trained on massive text datasets to understand and generate human-like language. It can answer questions, write essays, summarize documents, translate languages, write code, and more.

Popular LLMs in 2025 include:

• GPT-4o (OpenAI)
• Claude 3 (Anthropic)
• Gemini 1.5 (Google DeepMind)
• Mistral / Mixtral (Open-source)
• LLaMA 3 (Meta)

⚙️ The Core Architecture: Transformers

LLMs are built on a neural network architecture called the Transformer, introduced in the 2017 paper "Attention is All You Need".

🔄 Transformer Flow:

1. Input Text → Tokens: The input is split into tokens (usually subwords).
2. Embedding Layer: Each token is converted into a dense vector.
3. Positional Encoding: Since Transformers don’t process text sequentially, positional encodings give tokens a sense of order.
4. Self-Attention Layers: Each token can "attend to" every other token in the input to gather context.
5. Feedforward Layers: Add non-linearity and abstraction.
6. Stacked Layers: Deep LLMs use dozens or hundreds of such layers.
7. Output Prediction: The model predicts the next token (auto-regressively) based on all previous ones.

Self-Attention = Context Awareness The self-attention mechanism helps the model weigh which parts of the input are most relevant when generating a new token.

🏋️‍♂️ How Are LLMs Trained?

LLMs learn by predicting the next word in a sentence. They are trained on trillions of tokens from books, websites, code, conversations, and more.

🧩 Phases of Training:

• Pretraining: Trained on general text using objectives like next-token prediction or masked language modeling.
• Fine-tuning: Refined on specific tasks or domains (e.g., medical, legal).
• Instruction-Tuning: Trained to follow instructions like "summarize this" or "write code."
• RLHF (Reinforcement Learning with Human Feedback): Aligns responses with human preferences by ranking outputs.

💻 Requirements:

• Massive datasets (Common Crawl, Wikipedia, books)
• GPUs/TPUs with high memory (A100, H100, L40)
• Weeks or months of training time

🔣 Tokenization: The Input Language of LLMs

LLMs don’t read text like we do. Instead, they process input as tokens — chunks of words or characters.

Example:

"Understanding transformers" → ['Understanding', 'transform', 'ers']

Tokenizers (like SentencePiece, BPE) split input into a consistent format for embedding.

🧮 Sampling and Generation Techniques

When an LLM generates text, it predicts one token at a time. But it doesn’t just pick the highest probability every time.

🔄 Common Sampling Strategies:

• Greedy Search: Always picks the highest probability token
• Top-k Sampling: Considers the top k likely tokens
• Top-p (Nucleus) Sampling: Chooses from tokens whose combined probability mass is > p
• Temperature: Controls randomness (higher = more diverse output)

🤯 Why Do LLMs Hallucinate?

• LLMs don’t “know” facts — they generate based on patterns in training data
• If context is unclear or missing, they confidently generate plausible but incorrect answers
• That’s why grounding with RAG or external tools is important

⚖️ Open-Source vs Closed-Source LLMs

💡 Real-World Applications

• Chatbots (customer support, education, HR)
• Code Generation (Copilot, Cody, GPT Engineer)
• Data Analysis (text-to-SQL, pandas assistants)
• Search & Summarization (search engines, legal docs)
• Creative Work (music, lyrics, novels, design prompts)

🔮 The Future of LLMs

• Multimodal Models: LLMs like GPT-4o and Gemini handle text, image, audio, and video
• Smaller Efficient Models: Distilled, quantized models for on-device usage
• Personal AI Agents: Trained on your own documents, behavior, and preferences
• Better Alignment: Reducing bias, hallucinations, and ethical concerns

✅ Conclusion

Large Language Models are the foundation of modern AI — from chatbots to copilots to research tools. By understanding how they work, you’re better prepared to use, build, and even train your own models.

Stay tuned for follow-up articles on:

• Fine-tuning vs Prompt Engineering
• Comparing LLaMA 3, GPT-4o, and Claude 3
• Building your own mini-LLM with 1B parameters