Introduction
Welcome to a guide that aims to shed light on the inner workings of Large Language Models (LLMs), particularly in the context of customer interactions, such as those handled by Bland. This resource is designed for individuals who are relatively new to the world of LLMs. While we've aimed for clarity through analogies and simplifications, please remember that these are broad representations, and like any simplification, they come with exceptions.
Despite their seemingly remarkable intelligence, LLMs operate based on probability and pattern recognition, a stark contrast to human understanding.
Within this guide, we will explore fundamental concepts and definitions, from the initial step of breaking down words into numerical tokens to the generation of coherent responses. We will also touch upon critical considerations like hallucinations, security, and data privacy.
Ultimately, understanding that LLMs are sophisticated mathematical systems, rather than thinking entities, is key for organizations to deploy them responsibly and effectively, with realistic expectations about their capabilities and limitations.
Thank you for joining us on this journey of discovery!
What is an LLM?
A Large Language Model, or LLM, is essentially a machine that has learned to "speak" by processing an immense amount of written text – virtually everything ever written. While it doesn't possess human-like thought, it has absorbed enough linguistic patterns to understand how people sound when they do think. When you provide an LLM with a sentence, it can predict what is likely to come next. This isn't guesswork; it's based on the patterns it has identified within billions of conversations, books, and articles.
Think of an LLM as a highly knowledgeable assistant with an exceptional memory and without ego. It listens, it responds, and it continuously refines its abilities over time. This isn't magic – it's a combination of mathematics, vast memory, and a remarkably convincing performance.
Important Note: An LLM is not a person.
How an LLM Works: The Core Principles
At their heart, large language models don't comprehend language in the same way humans do. Instead, they operate on numbers.
The fundamental process involves:
- Breaking down words into smaller units called tokens.
- Converting these tokens into numbers.
- Using these numbers to calculate the most probable next number based on all the preceding words/numbers.
- Converting the resulting number back into a token (a small piece of a word).
Remember:
- LLMs are not people, and they are not alive.
- They cannot "think" in a human sense.
- Their capabilities are not "magic" but are largely based on complex, yet ultimately simple, mathematical operations performed on massive datasets.
1. Words → Numbers (Embeddings)
Large language models cannot directly process text; they first need to transform it into numbers. This process is known as embedding.
Consider the simple sentence:
“I need to check my account balance.”
Each word in this sentence is mapped to a set of hundreds of numbers. These numbers capture how that word is typically used in language. Imagine placing each word on a map where the distance between words reflects their semantic similarity. For instance, "account" might be positioned near words like "transaction" and "balance," while the meaning of "balance" itself could shift depending on the context (e.g., banking versus walking a tightrope).
These numerical patterns, called vectors, are what the LLM actually works with. Before any processing occurs, your input sentence is converted into this structured numerical format, which encapsulates the underlying meaning of the words.
You don't need to grasp the intricate mathematics to understand the core idea: the model perceives the relationships between words not as dictionary definitions, but as their relative positions within a high-dimensional numerical space.
2. Numbers → Math
Once the input is transformed into numbers, the LLM performs a series of calculations to determine the subsequent words. This is achieved using a specific type of neural network architecture called a transformer.
What is a Transformer? (The Foundation of LLMs)
A transformer is a crucial component that helps the model identify the most significant words within a sentence. It analyzes each word in its context – considering not only the words that precede it but also those that follow. For example, in the sentence:
“She deposited money into her account and checked the balance.”
The transformer enables the model to recognize that "balance" is related to both "account" and "money," not just the immediately preceding word.
Think of it as reading with an intelligent highlighter that automatically marks the most important words based on the overall meaning of the sentence. This is how the model maintains context and understanding, even in lengthy or complex sentences.
Through multiple layers of these transformer mechanisms, the model builds an internal representation of what is being communicated and calculates the most likely next word based on the patterns it has learned from its vast training data.
3. Math → Numbers → Words (Output Generation)
At this stage, the model has generated a ranked list of potential next words, each associated with a probability score. It selects the word with the highest probability (or sometimes a combination of top contenders) and converts its numerical representation back into a word.
This process is then repeated – the newly generated word is added to the sequence, and the model predicts the next word, and so on. This continues until a complete response is constructed, one word at a time.
For example:
Input: “I need to check my account balance.”
Output: “Sure, I can help with that. What’s your account number?”
To a human, this feels like a fluid and natural reply. However, beneath the surface, it's a continuous process of numbers being updated, probabilities being calculated, and the resulting numbers being translated back into language at a speed that creates the illusion of real-time conversation.
How the Model Represents Meaning (and Why Prompting Works)
When a language model encounters a word, it doesn't simply see it as a sequence of letters. Instead, it transforms it into a vector – a list of numbers that captures the word's meaning based on how it has been used across its extensive training data.
These vectors exist within a high-dimensional space – imagine a coordinate system with hundreds of dimensions instead of just the familiar two or three. Within this space:
- Words that frequently appear in similar contexts are positioned closer to each other.
- The word "bank" (in a financial context) is located near words like "loan" and "account."
- The word "bank" (referring to a river) resides in a completely different region of this space, close to words like "stream" and "shore."
This is how the model understands meaning: not through explicit definitions, but through the statistical relationships it has learned between words. Words become points in this multi-dimensional space, and their meaning is derived from their proximity to other words the model has encountered during training.
Now, here's the crucial part:
When you provide a prompt to an LLM, you're not just giving it a sentence – you're essentially defining a path through this vector space.
Each word in your prompt activates specific regions within this space, guiding the model towards certain directions. You are, in effect, steering it towards clusters of meaning that align with your intended output.
For example:
- Prompt: “Why did the customer cancel?”
Activates regions associated with common reasons for cancellation, such as dissatisfaction or cost. - Prompt: “Write legal language explaining a contract termination.”
Activates formal structures and vocabulary found in legal documents the model has encountered during its training.
In this way, your prompt acts like a coordinate seed. It anchors the model in a specific area of the vector space where the most relevant words, tones, and concepts are likely to be predicted.
Key Point: This is the fundamental reason why prompting is effective. The model's knowledge isn't stored as discrete facts; it's encoded as patterns within this vast vector space. Prompting is the mechanism through which you position the model in the specific region of that space where useful and relevant responses reside.
“How is the LLM so ‘smart’ or able to ‘figure things out’?”
This is a common point of confusion. When an LLM generates a surprisingly accurate statement or seems to "reason" about something it hasn't been explicitly trained on, it's vital to remember that this isn't some form of nascent intelligence or a "ghost in the machine."
LLMs excel because they can process enormous amounts of data and identify intricate connections between different pieces of information.
Consider this illustrative example:
Imagine asking an LLM:
"How might protein folding algorithms help improve traffic flow in cities?"
These two seemingly disparate domains – protein folding and urban traffic management – were likely not explicitly linked in the model's training data. Yet, the LLM might respond with something like:
"Both protein folding and traffic flow involve optimizing paths through complex, constrained environments. The Monte Carlo simulation methods used to predict protein structures could be adapted to model how vehicles navigate through congested urban grids, potentially identifying traffic pattern interventions that minimize overall system energy."
What's Actually Happening Here:
The LLM isn't exhibiting creative insight or a deep understanding of either domain. Instead:
- Its training data contains information where both domains are described in terms of optimization problems (leading to pattern similarity).
- Protein folding is described using specific algorithms and statistical methods, creating associations with those concepts.
- Traffic flow is also described in terms of path optimization, leading to similar associations.
- The underlying mathematical concepts related to "optimization," "pathfinding," and "complex systems" occupy similar regions within the model's vector space (its "brain," for lack of a better term).
The LLM bridges these concepts because the statistical patterns associated with these mathematical ideas appear in similar linguistic contexts across its training data. The model is essentially saying, "These patterns of tokens often appear together in similar types of discussions."
This is pattern recognition at a massive scale. The model doesn't truly "understand" traffic or proteins, but it can identify statistical similarities in how we write and talk about these concepts. The accuracy of these connections can be surprisingly high, which is a key reason why LLMs are so powerful: the sheer volume and diversity of human-written data allow for these unexpected, yet often accurate, leaps between concepts.
This ability to connect seemingly unrelated ideas explains why LLMs can sometimes generate insights that appear novel. They can identify relationships between domains that humans might not immediately consider because they process all information as mathematical relationships between concepts. This is an incredibly powerful and transformative capability, but it is fundamentally rooted in mathematics. This entire section serves to emphasize that impressive predictions from an LLM do not necessarily imply intelligence or explicit training on the specific connection being made.
However, it's crucial to remember that these connections are not validated by real-world testing or deep understanding. The LLM cannot determine if the connection it makes is actually useful or merely sounds plausible. It's an educated guess based on linguistic and conceptual similarity.
What Is Prompting?
Prompting is the act of providing specific instructions to an AI to guide its response.
How it works:
- Your words are converted into numbers within the model's system.
- These numbers activate specific patterns within the AI's neural network.
- Think of it like different areas of a "brain" lighting up in response to specific stimuli.
- These activated patterns influence which words the AI is most likely to choose next.
- Consider prompting like an experienced chef using a recipe. The recipe provides guidance, but the chef's existing knowledge and skills influence how they interpret and execute it.
Fine-Tuning:
While prompting offers temporary guidance, fine-tuning results in permanent changes to the model's internal workings:
- Prompting: Is akin to temporarily painting a car a different color.
- Fine-Tuning: Is like permanently adding a spoiler to the back of the car.
In fine-tuning, the model's internal numerical patterns are actually modified based on the specific examples you provide. This is similar to how your brain forms stronger neural connections when you repeatedly practice a skill.
For applications like phone agents in the banking sector, fine-tuning on conversations specific to banking means the AI develops specialized pathways within its neural network that are optimized for financial discussions. This allows it to handle these tasks more effectively and naturally, often without the need for overly detailed prompts each time.
The Reality of Hallucinations
It's important to understand that all large language models will hallucinate sometimes. This is an inherent limitation of current technology.
In any AI system, you might encounter situations where:
- It confidently states incorrect information.
- It fabricates plausible-sounding but entirely untrue details.
- It confuses similar concepts or "misremembers" information.
This limitation arises because LLMs do not truly "understand" information in a human sense. They are highly sophisticated pattern-matching systems that predict the most likely sequence of text based on their training data.
However, with the right tools and techniques, it is possible to significantly reduce the statistical likelihood of hallucinations occurring. Just as you can train and oversee a human employee to minimize errors.
Careful development and monitoring can bring the occurrence of incorrect AI outputs close to zero in practical applications.
To learn more about how Bland’s architecture protects against hallucinations and keeps customer data secure, read our deep dive on LLM Security and Data Privacy.
Key Terms:
- Tokens: The most basic units that LLMs process. Words are often broken down into smaller parts (tokens), which can be parts of words or symbols. These tokens are then converted into numbers for the model to work with. For example, "banking" might be a single token, while "extraordinary" could be broken into "extra" and "ordinary".
- Embeddings: The numerical representations of words in a multi-dimensional space. For instance, "Finance" might be represented by a list of numbers like [0.1273895, -0.3981265, 0.7264381...]. Embeddings are the model's way of "reading" words, similar to how computers read code as 1s and 0s. This numerical representation allows the model to understand the relationships between words.
- LLM (Large Language Model): A neural network designed to predict the most likely next token in a sequence based on the patterns it has learned from massive amounts of text data. These models contain billions of parameters (adjustable numerical values) that encode the statistical relationships between words and concepts.
- Fine-Tuning: The process of adapting a pre-trained LLM to perform better on specific tasks or datasets. For example, while a general LLM might understand basic banking terms, fine-tuning it on Chase's specific documentation would optimize its internal parameters for those particular financial discussions.
- Inference: The computational process by which the model generates responses based on input. During inference, the LLM calculates the probabilities of different tokens appearing next in real-time, selecting the most likely continuations based on the input and its learned patterns.
- Transformer: A neural network architecture that allows the model to weigh the importance of different parts of the input sequence when predicting the output. Unlike earlier models that primarily looked at the immediately preceding words, transformers can consider the entire context of the input, much like an experienced surgeon drawing on years of knowledge, not just the most recent case.
- Prompt: The input text provided to an LLM that sets the context for the desired response. The prompt is first broken down into tokens, then converted into embedding vectors, and finally processed through the model's neural network to influence the generated output.
- Hallucination: An instance where the model generates output that is factually incorrect or nonsensical, often presented with high confidence. This occurs because LLMs are fundamentally prediction-based systems, generating statistically plausible continuations rather than retrieving verified facts..
