A Data Engineer’s Guide to Understanding Large Language Models
If you’ve ever typed a prompt into ChatGPT, Claude, or Gemini and wondered, “How does this thing actually work?” — you’re not alone.
Large Language Models (LLMs) are the most talked-about technology in recent years. Yet what happens under the hood remains unclear to many engineers.
In this article, we’ll break down how LLMs work — from architecture to training to inference — so you understand not just what they do, but how and why they do it.
1. What Is an LLM, Really?
At its core, a Large Language Model is a neural network trained on massive amounts of text data. Its task is deceptively simple:
Predict the next token in a sequence.
When you type:
“The capital of France is”
The model does not “know” geography. Instead, it calculates probabilities based on patterns learned from billions of documents and determines that “Paris” is overwhelmingly the most likely next token.
That’s it.
Scaled up with billions (or trillions) of parameters, this simple prediction mechanism produces behaviour that feels remarkably intelligent.
An LLM is not a database of facts.
It is a sophisticated pattern-completion engine.
This distinction explains both its strengths and its limitations.
2. The Transformer Architecture: The Engine Inside
Modern LLMs are built on the Transformer architecture, introduced in the 2017 paper “Attention Is All You Need.”
Before Transformers, language models processed text sequentially, word by word. This made them slow and poor at handling long-range context.
The Transformer changed the game with a mechanism called self-attention.
Instead of reading text strictly left to right, the model can look at all words in a sequence simultaneously and determine which words are most relevant to each other.
Key Components of a Transformer
Tokenizer
Converts raw text into tokens. The word “unbelievable” might become “un”, “believ”, and “able”. Most models use subword tokenization so they can handle any text — including code and multiple languages.
Embedding Layer
Each token is converted into a high-dimensional vector (a list of numbers). In this vector space, similar concepts are positioned closer together — “king” and “queen” are closer than “king” and “pizza.”
Self-Attention Mechanism
For every token, the model computes attention scores against all other tokens to determine what context matters most. When processing “The bank by the river was muddy,” attention helps determine that “bank” refers to a riverbank, not a financial institution.
Feed-Forward Layers
These layers further transform each token’s representation, enabling the model to capture complex patterns and relationships.
Output Layer
Produces a probability distribution over the vocabulary, predicting what token should come next.
Modern models stack dozens — sometimes over 100 — of these layers, each refining the model’s understanding of context.
3. Training: How LLMs Learn
Training an LLM is a massive computational effort that can cost millions of dollars and require thousands of GPUs.
Stage 1: Pre-Training
The model is trained on trillions of tokens from books, websites, code repositories, and research papers.
During pre-training, the model learns to predict the next token. No human is labeling data. The system simply adjusts its parameters to minimize prediction errors.
At this stage, the model absorbs:
- Language structure
- World knowledge
- Reasoning patterns
- Programming syntax
But it is still just a raw text predictor. It does not yet behave like a helpful assistant.
Stage 2: Fine-Tuning with Human Feedback (RLHF)
This is where modern chatbots become conversational.
Through Reinforcement Learning from Human Feedback (RLHF), the model is trained to produce responses that humans prefer.
The process typically involves:
- Supervised Fine-Tuning using high-quality example conversations
- Training a reward model based on human rankings
- Reinforcement learning to optimize for helpful and safe outputs
Some labs also use Constitutional AI, where models are trained against defined principles instead of relying only on human comparisons.
This alignment step is what makes modern LLMs feel helpful rather than chaotic.
4. Inference: What Happens When You Send a Prompt
When you type a prompt, here’s what happens:
- Your text is broken into tokens.
- Tokens are converted into numerical vectors.
- The vectors pass through all Transformer layers.
- The model calculates probabilities for every possible next token.
- A token is selected based on a sampling strategy.
- That token is appended to the input.
- The process repeats.
This is called autoregressive generation.
The model generates one token at a time until it reaches a stop condition.
That’s why responses appear word-by-word — the model is literally generating them sequentially.
5. The Context Window: The Model’s Working Memory
LLMs do not have persistent memory like a database.
They operate within a fixed-size context window — a buffer that contains the current conversation.
Everything the model “remembers” exists inside this window.
Early models supported around 2,000 tokens. Modern systems can handle 100,000+ tokens, allowing them to process long documents or codebases.
However, the window is always finite. When it fills up, older content gets truncated.
For data engineers, this is similar to working with a fixed-size buffer or a sliding window in a streaming pipeline. You can only process what fits inside the current window.
6. Why LLMs Hallucinate
LLMs are next-token predictors, not fact-retrieval systems.
They are optimized for fluency and coherence — not truth.
If the model lacks reliable context, it will still generate the most statistically plausible continuation. That can result in confident but incorrect answers.
This is why Retrieval-Augmented Generation (RAG) is essential in production systems. By retrieving relevant data from a database or document store and injecting it into the prompt, you ground the model’s response in real, verified information.
LLM alone is powerful.
LLM + retrieval is reliable.
7. Techniques Powering Modern LLMs
The ecosystem is evolving rapidly. Some key advancements include:
Chain-of-Thought Reasoning
Encourages models to reason step-by-step, improving performance on complex logic and math tasks.
Tool Use and Function Calling
LLMs can now call APIs, execute code, query databases, and interact with external systems.
Mixture of Experts (MoE)
Instead of activating all parameters for every token, the model routes tokens to specialized sub-networks, improving efficiency.
Retrieval-Augmented Generation (RAG)
Combines LLMs with external knowledge bases for grounded, up-to-date responses.
Multimodality
Modern models can process text, images, audio, and code — extending beyond pure language tasks.
8. The Scale Behind Modern LLMs
To understand why only a few organizations build frontier models, consider the scale:
- Parameters: Hundreds of billions to over a trillion
- Training data: Trillions of tokens
- Training cost: Tens to hundreds of millions of dollars
- Hardware: Tens of thousands of GPUs
- Context window: Up to 200,000 tokens
The computational and financial barriers to entry are enormous.
9. Practical Takeaways for Engineers
Understanding LLM internals directly influences how you build systems with them.
Prompt engineering is really context engineering. The quality and structure of context determine output quality.
RAG is non-negotiable for production use cases involving domain-specific or factual data.
Temperature controls determinism. Use low temperature for SQL generation or data extraction. Use higher temperature for creative tasks.
Token limits are real constraints. Smart chunking and focused context often outperform simply stuffing more data into the window.
Always build evaluation pipelines. LLM outputs are probabilistic and must be tested for accuracy, consistency, and safety before deployment.
Conclusion
LLMs are not magic.
They are large-scale statistical systems built on the Transformer architecture, trained on vast datasets, and refined through human feedback.
Understanding how they work demystifies them — and more importantly, helps engineers design reliable, production-grade AI systems.
As data and AI engineers, our role is not just to use these models, but to integrate them responsibly into real-world systems.
And that starts with understanding what’s happening behind the scenes.