Foundation Models in LLMs: What They Are and Why They Matter
What Makes AI Like ChatGPT So Powerful?
Why can some AI systems write fluent paragraphs, translate text across dozens of languages, and even debug code within seconds? The answer lies in something called a foundation model. These models, built using transformer architecture, are at the heart of today’s most capable AI systems.
If you’ve ever wondered how these models work under the hood, this is the blog post for you. By the end, you’ll understand the technical foundations of modern AI, from tokenization and embeddings to self-attention and multi-layered neural networks.
Foundation Models Explained: What Are They and Why Do They Matter?
To understand how foundation models are revolutionizing AI, we need to start at the surface. These models aren’t just smart, they’re built to generalize, adapt, and perform across countless tasks.
Foundation models are massive AI systems trained on enormous datasets like books, websites, academic papers, and even code repositories. Rather than being trained for one specific task, they learn general patterns and relationships in data. This makes them adaptable and powerful across a wide range of downstream tasks.
Examples of well-known foundation models include:
Why do they matter?
Because they’re general-purpose. Once trained, they can be fine-tuned to:
- Answer customer service questions
- Generate creative writing
- Translate between languages
- Summarize long documents
- Analyze sentiment in product reviews
These are not narrow tools. They’re foundational.
Key traits:
- Trained on multi-domain data
- Massive in scale (billions of parameters)
- Built using the transformer architecture
Curious how these models can be applied in your business workflows?
Explore our custom AI solutions in Ottawa.
Transformer Architecture in Foundation Models: The Core Innovation
At the heart of foundation models lies a game-changing innovation: the transformer. This architecture changed the way machines understand language—and later, images, audio, and code. But what makes transformers so special? Let’s unpack their mechanics.
What do all these models have in common?
They’re built on transformers.
The transformer is a neural network architecture introduced in 2017 by the paper “Attention Is All You Need.” Unlike earlier models like RNNs (recurrent neural networks) or LSTMs (long short-term memory), which processed words sequentially, transformers process data in parallel. This means they can scale faster, understand longer contexts, and outperform in virtually every language task.
Let’s break it down.
Tokens: How Data Gets Ingested
Before any processing happens, the input text is broken down into tokens.
A token might be a word, character, or subword (e.g., “unbelievable” becomes “un”, “believ”, “able”).
Each token is assigned a unique identifier in a vocabulary.
The model doesn’t read letters or grammar. It reads token IDs.
This tokenization process makes it possible to convert raw text into a form that a neural network can understand.
Embeddings: Turning Words into Vectors
Once the tokens are created, the model translates each token into a vector. This is called an embedding.
Think of an embedding as a list of numbers that represent meaning.
Words with similar meanings have vectors that are close together in space.
These vectors become the input to the next layers of the model.
So “cat” and “kitten” might have similar embeddings. “Cat” and “finance”? Not so much.
Encoding: Capturing Context Within Input
Unlike a simple bag-of-words model, transformers need to understand order.
They apply positional encoding to let the model know where each word appears in a sentence.
This helps distinguish “the dog chased the cat” from “the cat chased the dog.”
Encoding ensures that both the meaning and the sequence of language are captured.
Looking to integrate natural language processing into your tools?
Get in touch with our Ottawa AI Experts.
Self-Attention: The Mechanism That Makes Transformers Powerful
The secret weapon behind transformer models is self-attention. It’s the mechanism that lets models determine which parts of an input are most important—regardless of their position. Here’s why it’s such a big deal.
Self-attention is what gives transformers their edge.
It allows the model to focus on different parts of the input depending on what’s most relevant to each token.
Here’s how it works:
- For each token, the model calculates how much attention it should pay to every other token in the sentence.
- This generates attention scores that are used to create new contextualized vectors.
- The process is done through attention heads, each looking at the sentence differently.
Why is this powerful?
Because it means the model can:
- Learn dependencies between far-apart words
- Understand hierarchical structure
- Adapt to different contexts dynamically
In simpler terms: it knows when “bass” refers to a fish versus a guitar.
Want AI that understands your data as well as your team does?
Learn more about our custom AI solutions in Ottawa.
Encoders and Decoders: The Building Blocks
Transformers are built from two key components: encoders and decoders. Each plays a specific role in how models understand and generate language. Let’s take a closer look at how they work together.
The transformer is built from stacks of encoders and decoders.
Encoders
- Handle the input.
- Convert token vectors into deeper representations.
- Used in models like BERT for classification and understanding tasks.
Decoders
- Handle the output.
- Generate new tokens based on previous input.
- Used in models like GPT for generation tasks.
Encoder-Decoder Models
- Combine both: input goes through encoder; output is generated by decoder.
- Common in translation models like T5 and BART.
Need help understanding which model architecture fits your application?
Our AI Consultants in Ottawa can provide guidance for your implementation. Book a consultation.
Layers and Feedforward Networks
Once you understand encoders and attention, the next piece of the puzzle is how these models go deeper. Foundation models rely on stacks of layers that gradually build more abstract and useful representations of the data.
Inside each encoder and decoder are layers.
Each layer contains:
- A self-attention mechanism
- A feedforward neural network (FFN)
- Layer normalization and residual connections
The more layers, the more depth the model must learn abstract relationships.
- A small model might have 6 layers.
- GPT-4? Is rumoured to have 100+.
Feedforward networks are what refine the data at each step:
- They transform the attention output into a new form.
- Each token is processed individually.
Together, self-attention and feedforward layers build a deeply contextual understanding of input.
Why This Architecture Works So Well
The brilliance of the transformer isn’t just in how it processes data; it’s in how well it scales and adapts. Let’s look at the reasons this architecture has become the foundation of today’s most capable AI systems.
The transformer design works because it is:
- Scalable: Processes input in parallel instead of one word at a time.
- Flexible: Can handle language, code, audio, and more.
- Context-Aware: Captures long-range dependencies better than older models.
- Pretrained: Once trained, can be fine-tuned for nearly any task.
What makes foundation models so adaptable across industries:
- Law firms use them for document summarization.
- Developers use them to autocomplete code.
- Analysts use them to extract insights from PDFs.
Want to put the power of foundation models to work?
Reach out to explore deployment and fine-tuning options.
Final Thoughts: Architecture Is the Future of AI
From tokenization to multi-head attention, transformers represent the evolution of how machines learn from language. The future of AI isn’t just generative, it’s architectural.
Talk to our experts about deploying your own transformer-powered AI model.