An LLM is a reflection of the data it learns from; 'garbage in, garbage out' on a massive scale.
Large Language Models 101
In our last article, we saw what Large Language Models can do. Now, let’s peek under the hood to understand how they work. Forget the impenetrable jargon and complex math. This is your beginner-friendly guide to the core concepts that allow an AI to write code, answer questions, and even reason about complex problems.
From Autocomplete to Intelligence
For years, your phone’s keyboard has been able to predict the next word in your sentence. This is simple statistical analysis based on common word pairings. An LLM operates on a similar principle but on a vastly more sophisticated scale.
The leap isn’t just in the volume of data, but in the model’s ability to understand context. It doesn’t just guess the next word; it understands the grammar, semantics, and even the underlying sentiment of the entire conversation or document before making a prediction. This is the crucial jump from autocomplete to artificial intelligence.
The Transformer Breakthrough
The engine that drives modern LLMs is an architecture called the Transformer, introduced in 2017. It revolutionized AI by solving two major problems: speed and context.
Older models processed text sequentially, reading one word at a time. This was slow and caused them to “forget” the beginning of a long sentence by the time they reached the end. The Transformer, however, can process all the words in a sequence at once (parallel processing).
Its true innovation is a mechanism called self-attention. This allows the model to weigh the importance of every other word in the input when considering a single word. It learns the subtle relationships and dependencies, no matter how far apart the words are.
Mini-Exercise: Thinking in Parallel
Imagine you highlight the following function and ask an AI to predict the next line:
def calculate_user_metrics(user_id):
posts = fetch_posts_by_user(user_id)
comments = fetch_comments_by_user(user_id)
# The AI's turn to predict
An old model might guess something generic like return posts.
A Transformer-based LLM uses self-attention to see the whole picture at once. It gives high “attention” to calculate_user_metrics, posts, and comments. It recognizes the pattern of fetching user-related data and concludes that the logical next step is to combine them. It would therefore predict something far more useful, like: return len(posts) + len(comments). It understood the intent of the code, not just the last word.
Pre-Training vs. Fine-Tuning
An LLM is built in two main phases:
Pre-Training: This is the foundational, resource-intensive stage. The model is trained on a massive, general dataset—a huge portion of the public internet, books, and trillions of words and code tokens. The goal here is for the model to learn the fundamental patterns of language, grammar, logic, and reasoning. The result is a powerful but generic “base model.”
Fine-Tuning: This is a secondary, more specialized training phase. We take the pre-trained base model and train it further on a smaller, curated dataset specific to a particular task. For example, a company might fine-tune a model on its internal documentation and codebase to create an expert assistant that understands its proprietary software.
Why Training Data Quality Matters
An LLM is a reflection of the data it learns from. If the training data is flawed, the model will inherit those flaws. This is the “garbage in, garbage out” principle on a massive scale.
If a model is pre-trained on millions of lines of open-source code containing common security vulnerabilities (like SQL injections or outdated encryption), it will learn to replicate those bad practices. It doesn’t know the code is insecure; it only knows it’s a common pattern. This is why human oversight and rigorous testing are non-negotiable when implementing AI-generated code.
Glossary: Key Terms to Know
- Token: The fundamental building block of text for an LLM. A token can be a word, part of a word (like “ing”), or a piece of punctuation. For example, the phrase “AI-powered coding” might be broken into four tokens:
AI,-,powered,coding. - Context Window: The amount of tokenized text the model can “see” at one time. A model with a small context window might only remember the last few paragraphs, while one with a large window can analyze entire documents or codebases at once.
- Few-Shot Prompting: The practice of giving the model a few examples of what you want before you ask your actual question. This helps the model understand the desired format and style of the output, leading to much more accurate results.