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Summary – Understanding the core workings of LLMs raises issues: tokenization granularity mismatches, lack of genuine semantic understanding, hallucinations, heavy compute demands, cross-language inefficiencies, bias propagation, overfitting risk, integration complexity;
Solution: define optimal tokenization schemes → audit and debias pipelines → deploy modular fine-tuning loops.
In a landscape where generative AI is spreading rapidly, many leverage its outputs without understanding its inner workings. Behind every GPT-4 response lies a series of mathematical and statistical processes based on the manipulation of tokens, weights, and gradients. Grasping these concepts is essential to assess robustness, anticipate semantic limitations, and design tailored use cases. This article offers a hands-on exploration of how large language models operate—from tokenization to fine-tuning—illustrated by real-world scenarios from Swiss companies. You will gain a clear perspective for integrating generative AI pragmatically and securely into your business processes.
Understanding LLM Mechanics: From Text to Predictions
An LLM relies on a transformer architecture trained on billions of tokens to predict the next word. This statistical approach produces coherent text yet does not grant the model true understanding.
What Is an LLM and How It’s Trained
Large language models (LLMs) are deep neural networks, typically based on the Transformer architecture. They learn to predict the probability of the next token in a sequence by relying on attention mechanisms that dynamically weight the relationships between tokens.
Training occurs in two main phases: self-supervised pre-training and, sometimes, a human-supervised step (RLHF). During pre-training, the model ingests vast amounts of raw text (articles, forums, source code) and adjusts its parameters to minimize prediction errors on each masked token.
This phase demands colossal computing resources (GPU/TPU units) and time. The model gradually refines its parameters to capture linguistic and statistical structures, yet without an explicit mechanism for true “understanding” of meaning.
Why GPT-4 Doesn’t Truly Understand What It Says
GPT-4 generates plausible text by reproducing patterns observed during its training. It does not possess a deep semantic representation nor awareness of its statements: it maximizes statistical likelihood.
In practice, this means that if you ask it to explain a mathematical paradox or a moral dilemma, it will rely on learned formulations rather than genuine symbolic reasoning. Its errors—contradictions, hallucinations—stem precisely from this purely probabilistic approach.
However, its effectiveness in drafting, translating, or summarizing stems from the breadth and diversity of its training data combined with the power of selective attention mechanisms.
The Chinese Room Parable: Understanding Without Understanding
John Searle proposed the “Chinese Room” to illustrate that a system can manipulate symbols without grasping their meaning. From the outside, one obtains relevant responses, but no understanding emerges internally.
In the case of an LLM, tokens flow through layers where linear and non-linear transformations are applied: the model formally connects character strings without any internal entity “knowing” what they mean.
This analogy invites a critical perspective: a model can generate convincing discourse on regulation or IT strategy without understanding the practical implications of its own assertions.
Example: A mid-sized Swiss pension fund experimented with GPT to generate customer service responses. While the answers were adequate for simple topics, complex questions about tax regulations produced inconsistencies due to the lack of genuine modeling of business rules.
The Central Role of Tokenization
Tokenization breaks text down into elemental units (tokens) so the model can process them mathematically. The choice of token granularity directly impacts the quality and information density of predictions.
What Is a Token?
A token is a sequence of characters identified as a minimal unit within the model’s vocabulary. Depending on the algorithm (Byte-Pair Encoding, WordPiece, SentencePiece), a token can be a whole word, a subword, or even a single character.
In subword segmentation, the model merges the most frequent character sequences to form a vocabulary of hundreds of thousands of tokens. The rarest pieces—proper names, specific acronyms—become concatenations of multiple tokens.
Processing tokens allows the model to learn continuous representations (embeddings) for each unit, facilitating similarity calculations and conditional probabilities.
Why Is a Rare Word “Split”?
The goal of LLMs is to balance lexical coverage and vocabulary size. Including all rare words would increase the dictionary and computational complexity.
Tokenization algorithms thus split infrequent words into known subunits. This way, the model can reconstruct the meaning of an unknown term from its subwords without needing a dedicated token.
However, this approach can degrade semantic quality if the split does not align properly with linguistic roots, especially in inflectional or agglutinative languages.
Tokenization Differences Between English and French
English, being more isolating, often yields whole-word tokens, whereas French, rich in endings and liaison, produces more subword tokens. This results in longer token sequences for the same text.
Accents, apostrophes, and grammatical elisions (elision, liaison) involve specific rules. A poorly tuned model may generate multiple tokens for a simple word, reducing prediction fluency.
A bilingual integrated vocabulary, with optimized segmentation for each language, improves model coherence and efficiency in a multilingual context.
Example: A Swiss machine tool manufacturer operating in Romandy and German-speaking Switzerland optimized the tokenization of its bilingual technical manuals to reduce token count by 15%, which accelerated the internal chatbot’s response time by 20%.
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Weights, Parameters, Biases: The Brain of AI
The parameters (or weights) of an LLM are the coefficients adjusted during training to link each token to its context. Biases, on the other hand, steer statistical decisions and are essential for stabilizing learning.
Analogies with Human Brain Functioning
In the human brain, modifiable synapses between neurons strengthen or weaken connections based on experience. Similarly, an LLM adjusts its weights on each virtual neural connection.
Each parameter encodes a statistical correlation between tokens, just as a synapse captures an association of sensory or conceptual events. The larger the model, the more parameters it has to memorize complex linguistic patterns.
To give an idea, GPT-4 houses several hundred billion parameters, far more than the human cortex counts synapses. This raw capacity allows it to cover a wide range of scenarios, at the cost of considerable energy and computational consumption.
The Role of Backpropagation and Gradient
Backpropagation is the key method for training a neural network. With each prediction, the estimated error (the difference between the predicted token and the actual token) is propagated backward through the layers.
The gradient computation measures how sensitive the loss function is to changes in each parameter. By applying an update proportional to the gradient (gradient descent method), the model refines its weights to reduce overall error.
This iterative process, repeated over billions of examples, gradually shapes the embedding space and ensures the model converges to a point where predictions are statistically optimized.
Why “Biases” Are Necessary for Learning
In neural networks, each layer has a bias term added to the weighted sum of inputs. This bias allows adjusting the neuron’s activation threshold, offering more flexibility in modeling.
Without these biases, the network would be forced through the origin of the coordinate system during every activation, limiting its capacity to represent complex functions. Biases ensure each neuron can activate independently of a zero input signal.
Beyond the mathematical aspect, the notion of bias raises ethical issues: training data can transmit stereotypes. A rigorous audit and debiasing techniques are necessary to mitigate these undesirable effects in sensitive applications.
Fine-Tuning: Specializing AI for Your Needs
Fine-tuning refines a generalist model on a domain-specific dataset to increase its relevance for a particular field. This step improves accuracy and coherence on concrete use cases while reducing the volume of data required.
How to Adapt a Generalist Model to a Business Domain
Instead of training an LLM from scratch, which is costly and time-consuming, one starts from a pre-trained model. You then feed it a targeted corpus (internal data, documentation, logs) to adjust its weights on representative examples.
This fine-tuning phase requires minimal but precise labeling: each prompt and expected response serve as a supervised example. The model thus incorporates your terminology, formats, and business rules.
You must maintain a balance between specialization and generalization to avoid overfitting. Regularization techniques (dropout, early stopping) and cross-validation are therefore essential.
SQuAD Formats and the Specialization Loop
The SQuAD (Stanford Question Answering Dataset) format organizes data as question‐answer pairs indexed within a context. It is particularly suited for fine-tuning tasks like internal Q&A or chatbots.
You present the model with a text passage (context), a targeted question, and the exact extracted answer. The model learns to locate relevant information within the context, improving its performance on similar queries.
In a specialization loop, you regularly feed the dataset with new production-validated examples, which correct drifts, enrich edge cases, and maintain quality over time.
Use Cases for Businesses (Support, Research, Back Office…)
Fine-tuning finds varied applications: automating customer support, extracting information from contracts, summarizing reports, or conducting sector analyses. Each case relies on a specific corpus and measurable business objective.
For example, a Swiss logistics firm fine-tuned an LLM on its claims management procedures. The internal chatbot now answers operator questions in under two seconds, achieving a 92% satisfaction rate on routine queries.
In another scenario, an R&D department used a finely tuned model to automatically analyze patents and detect emerging technological trends, freeing analysts from repetitive, time-consuming tasks.
Mastering Generative AI to Transform Your Business Processes
Generative AI models rely on rigorous mathematical and statistical foundations which, once well understood, become powerful levers for your IT projects. Tokenization, weights, backpropagation, and fine-tuning form a coherent cycle for designing custom, scalable tools.
Beyond the apparent magic, it’s your ability to align these techniques with your business context, choose a modular architecture, and ensure data quality that will determine AI’s real value within your processes.
If you plan to integrate or evolve a generative AI project in your environment, our experts are available to define a pragmatic, secure, and scalable strategy, from selecting an open-source model to production deployment and continuous specialization loops.
