LLMs are Not Stochastic Parrots: How Large Language Models Actually Work

Where is AI heading? From my experience building AI applications, we’re witnessing a profound shift: AI is moving from the cloud to our devices. The open-source community is driving an arms race toward more efficient, powerful local models. This isn’t just about technical innovation—it’s about democratizing AI while preserving privacy.

But to understand this revolution, we first need to understand what a Large Language Model (LLM) truly is. Many dismiss them as “fancy autocomplete” or “stochastic parrots,” but this description misses the forest for the trees. The inner workings of models like GPT-4, Llama 3, or the ones running locally in BastionChat are far more sophisticated.

Let’s demystify the magic.

Part 1: The Foundation - How Words Become Meaningful Numbers

Before a Large Language Model (LLM) can understand language, it must convert text into a format it can process: numbers. This is arguably the most critical and magical part of the entire system. It happens in two stages: tokenization and embedding.

1. Tokenization: Breaking Down Language First, the model breaks down a sentence into pieces called “tokens.” These can be words, parts of words (like run and ning), or punctuation.

Example: "The cat sat on the mat." might become ["The", "cat", "sat", "on", "the", "mat", "."]

2. Embeddings: Creating a Universe of Meaning Next, each token is converted into a long list of numbers—a high-dimensional vector called an “embedding.” This vector represents the token’s position in a vast “semantic space.” Think of it as a multi-dimensional map where proximity equals semantic similarity. On this map, “cat” and “kitten” are neighbors, while “cat” and “car” are miles apart.

But how are these powerful embeddings created? They aren’t programmed by humans; they are learned.

How Models Learn to Create Embeddings

Specialized models are trained on massive amounts of text with the sole purpose of creating high-quality embeddings. Their goal is to learn the relationships between words based on the contexts in which they appear.

A Pioneering Idea: Word2Vec One of the early breakthroughs, trained in 2013, was a model called Word2Vec. It used a simple but powerful idea: a word is defined by the company it keeps. It would take a sentence, remove a word, and then train the model to predict the missing word based on its neighbors. By doing this billions of times, it learned to place words that appeared in similar contexts close to each other in the semantic space.

The Revolution: BERT and Bidirectional Context A more recent and powerful model is BERT (Bidirectional Encoder Representations from Transformers). Unlike older models that read text from left to right, BERT reads the entire sentence at once, allowing it to understand context from both directions.

BERT was trained using a clever technique called the Masked Language Model (MLM) objective:

1. Take a sentence: "The cat sat on the mat."
2. Randomly “mask” (hide) a word: "The cat sat [MASK] the mat."
3. The model’s only job is to predict the hidden word, "on".

To get good at this game, BERT had to learn incredibly deep nuances of language and grammar. It learned that whatever fills the blank must be a preposition that fits between “sat” and “the.” This process forced it to create embeddings that were not just about the words themselves, but about the roles words play in a sentence. These are called contextual embeddings, a major leap forward from the static embeddings of older models.

The Result: Knowledge as Geometry

After training, this semantic space is no longer just a random collection of points. It has a geometric structure that encodes real-world knowledge and relationships. This allows us to perform “concept arithmetic” with vectors. The most famous example is:

Vector('King') - Vector('Man') + Vector('Woman') ≈ Vector('Queen')

This works because the vector difference between King and Man captures the abstract concept of “royalty.” Adding that “royalty” vector to Woman lands you right next to Queen in the semantic space. This demonstrates that the model hasn’t just memorized definitions; it has learned the underlying relationships that govern our world.

These embeddings are the secret sauce. They are learned during the training process, and they place words with similar meanings in similar locations in a vast “semantic space.” In this space, the vector for “king” minus the vector for “man” plus the vector for “woman” famously results in a vector very close to “queen.”

This numerical representation is what allows the mathematical machinery of the Transformer to work its magic.

Part 2: The Transformer - A Factory for Understanding Context

Now that we’ve mapped words into a semantic space, how does the model process and reason with this understanding? Enter the Transformer.

The Transformer architecture is not one monolithic block, but a stack of them—in models like GPT-3, as many as 96 blocks are stacked one on top of the other. The output of one block becomes the input for the next, allowing for progressively more sophisticated understanding.

Each block has two main jobs, performed by two key components. Let’s build an intuition for these before looking at the whole block.

A. Multi-Head Self-Attention: A Team of Specialists

Self-Attention is the star of the show. It answers a critical question for every word: Which other words in this sentence are most important to help me understand my own meaning?

Instead of one attention mechanism, an LLM uses Multi-Head Attention. Imagine a team of specialists all listening to the same sentence, but with different goals. Each “head” conducts its own self-attention process independently, looking for different kinds of relationships.

Click on each “Specialist Head” in the diagram below to see which words it focuses on to understand a different aspect of the sentence’s meaning.

As you can see, their findings are combined to give the model a much deeper, more nuanced understanding of the text than a single attention mechanism ever could.

B. Feed-Forward Network: The Processing Unit

After the attention mechanism has gathered context, the resulting vectors are passed to a Feed-Forward Network (FFN). You can think of this as a processing unit that “thinks” about the rich contextual information it just received, transforming it into a format that’s ready for the next Transformer block.

The Complete Picture: The Transformer Block in Action

Now that you understand the key components, let’s see them work together. The animation below shows a single data vector for one word as it gets processed and transformed by a complete Transformer block, incorporating both the Self-Attention and Feed-Forward stages.

By passing a word’s vector through this process, and then stacking these blocks 96 times, the model builds an incredibly deep and nuanced understanding of not just the word itself, but its entire role and context within the given text.

Part 3: Training - From Blank Slate to Know-It-All

We now have the two key ingredients:

Phase 1: Pre-training - Learning the World

The primary objective during pre-training is deceptively simple: predict the next token. The model is shown trillions of words of text from the public internet, books, and scientific articles, and it constantly tries to guess what word comes next.

The visualization below demonstrates one cycle of this “guess-and-adjust” process. Click the button to see how the model learns from its mistakes.

This loop consists of four key steps:

1. The Guess: The model processes a sequence and produces a list of probabilities for the next token.
2. The Reality Check: The model is shown the correct answer.
3. Calculating the Error (Loss): A “loss function” measures how wrong the model’s prediction was. A high loss means a bad guess.
4. Learning from Mistakes (Backpropagation & Gradient Descent): The model uses the loss score to see which internal parameters were most responsible for the error. It then makes tiny adjustments to those parameters, nudging them in the right direction to make a better guess next time.

By repeating this process trillions of times, the model is forced to learn the underlying patterns of language, grammar, and even the world itself. To get good at predicting the next word, it has no choice but to implicitly learn that “France” and “Paris” are related, that water is wet, and that subjects should agree with their verbs.

Phase 2: Fine-Tuning - Learning to Be Helpful

After pre-training, the model is a vast repository of knowledge, but it doesn’t know how to be a helpful assistant. It just knows how to complete text. The second phase, fine-tuning, teaches it to follow instructions and be conversational.

During this phase, the model is trained on a smaller, high-quality dataset of conversations, questions and answers, and instructions. This process often involves techniques like RLHF (Reinforcement Learning from Human Feedback), which works like a coach: humans rank different model responses, teaching it to align its outputs with nuanced human preferences and values. This is what turns a raw “knowledge engine” into a safe and useful tool.

But When Does It End? The Off-Ramp for Learning

That’s an excellent question. The training process isn’t an infinite loop churning away forever. If it were, the model might simply memorize every single sentence in its training data, making it a brilliant student of the past but terrible at handling any new, unseen information. This critical failure is known as overfitting.

To avoid this, and to create a model that can generalize its knowledge, the training process has several well-defined “off-ramps”:

1. Performance Plateau (Validation Loss): While the model trains on one massive dataset, engineers constantly check its performance against a separate, hidden dataset (the “validation set”). When the model’s performance on this validation set stops improving—or worse, starts getting worse—it’s a clear sign that it’s beginning to memorize rather than learn. This is the most common and important stopping point.
2. Reaching a Target (Convergence): The “loss” value we saw in the animation is a measure of the model’s error. Training might be set to stop once this error value drops below a certain predefined threshold.
3. Fixed Duration (Epochs): Sometimes, the process is simply set to run for a fixed number of cycles (epochs) through the training data, based on budget, time, or prior experience with similar models.
4. Manual Intervention: Ultimately, the human engineers overseeing the process can decide to halt training at any point if they are satisfied with the model’s capabilities.

Once training is complete, the model’s weights are frozen. It is no longer learning. It is now a static, highly knowledgeable artifact, ready to be deployed for inference—the process of actually generating answers to our prompts.

Part 4: The Generative Dance - How LLMs Write

So far, we’ve focused on how an LLM understands text. But how does it actually generate the answers that feel so conversational? It’s an elegant, step-by-step process called autoregressive generation.

Think of it like an author writing a novel one word at a time. They write a word, re-read the sentence, and then decide on the next word based on everything that came before. An LLM does exactly this, but at lightning speed.

The interactive visualization below shows this loop in action. Click the button to see how the model generates a response one token at a time.

This loop involves a few key steps:

1. Initial Processing: The model first processes your entire prompt using the full Transformer architecture we’ve described.
2. The First Prediction: The model generates a probability score for every possible next token.
3. Sampling: The model selects a token from this list. It doesn’t always pick the most probable one; a bit of randomness, controlled by a “temperature” setting, makes the output feel more natural and creative. A low temperature makes the model more deterministic and focused, while a high temperature encourages more diverse and unexpected results.
4. The Loop Begins: The newly chosen token is appended to the original input. This new, longer input is then fed back into the model to generate the next token. This continues until it generates a special [END_OF_TEXT] token. For efficiency, modern LLMs use an optimization called KV Caching, where the intermediate calculations (“Key” and “Value” vectors) from previous steps are saved. This means the model only needs to perform the full computation for the newest token, making generation much faster.

This iterative process is why an LLM is not a parrot. Unlike a parrot that simply repeats trained phrases, the model dynamically regenerates its response one token at a time by re-evaluating the entire context at every step. This enables it to produce truly novel and coherent answers that are tailored to the specific conversation.

Part 5: Putting It All Together - An End-to-End Example

We’ve covered all the individual pieces, from embeddings to generation. Now, let’s trace a complete interaction from prompt to final answer with one final visualization that shows how all these systems work in concert.

As the animation shows, the process flows logically:

1. Understanding the Input:

• Tokenization & Embedding: Your prompt is broken down into tokens and converted into a sequence of vectors that represent its meaning.
• Transformer Layers: These vectors are processed through the entire stack of Transformer blocks. The self-attention heads analyze relationships, and by the final layer, the model has produced a set of vectors representing a deep, contextual understanding of your request.

2. Generating the Response:

• The final, contextualized vectors are passed to the Generative Loop.
• The model generates the response one token at a time, feeding the output back into the input at each step, until the full answer is constructed.

Each word is chosen based on the initial prompt plus every single word that came before it. This allows the model to build complex, coherent sentences that directly address the user’s query.

Conclusion: From Parrots to Partners

To see a Large Language Model as a “stochastic parrot” is to see only the final word it types. It ignores the immense, context-aware machinery operating beneath the surface: the semantic understanding built by embeddings, the web of relationships revealed by self-attention, and the deliberate, step-by-step construction of a response.

These are not just complex autocomplete systems. They are powerful pattern-matching engines capable of building and maintaining rich contextual models of our language and ideas.

The most exciting frontier is that this power no longer requires massive data centers. While cloud models prioritize scale, local AI optimizes for intimacy. Through innovation, sophisticated models can now run on our own devices, trading a fraction of raw power for uncompromising privacy—your data never has to leave your device. This shift represents a fundamental change in our relationship with AI, from a cloud-based service to a personal, private partner.

When every token is processed on your device—using the same Transformer architecture we’ve unpacked—AI stops being a cloud service and becomes an extension of your mind. That’s the future BastionAI is building.

Experience the future of private AI with BastionChat on the App Store