What is an LLM (large language model)?
Series of articles on AI
This is the first article in a series of four:
- LLMs: understanding what they are and how they work (this article).
- NLP: exploring Natural Language Processing.
- AI Agents: discovering autonomous artificial intelligences.
- Comparison and AI Smarttalk’s positioning: an overall synthesis and perspective.
Imagine a field of wildflowers stretching as far as the eye can see, where an oversized swarm of bees is busily buzzing around. They flutter, gather pollen from every bloom, and turn it into incredibly complex honey. That honey is language. And these bees are the LLMs (Large Language Models), those giant language models that work tirelessly to transform vast amounts of textual data into something structured, coherent, and sometimes even highly creative.
In this article, we will dive deep into the bustling hive of LLMs: understanding how these massive bees build and refine their honeycombs (their architecture), what types of pollen they collect (the data), how they coordinate to produce honey (text generation), and finally how to guide and tame these swarms so they deliver a sweet, well-crafted nectar rather than a random substance.
We will cover several key points:
- The origins and definition of an LLM
- Training techniques and the role of attention
- Concrete use cases and limitations
- Ethical, energy, and technical challenges
- Prompt engineering to get the best out of an LLM
- Deployment and maintenance options
We will push the bee analogy quite far. You might find the image of a bee gentle and harmless, but remember that a poorly managed swarm can still inflict quite a few stings. Before we light the smoke to calm them down, let’s explore the very structure of an LLM, which will no longer hold many secrets once you’ve finished reading.
To start, here is a simplified diagram (with no extra commentary) of the path a piece of text takes within an LLM, from input to output, passing through all the key steps:
1. What is an LLM? The swarm that buzzed louder than all the others
1.1. Origin and concept
For several years, Artificial Intelligence research has focused on natural language: how can we make a model understand and generate relevant text? Initially, we used NLP (Natural Language Processing) techniques based on simple rules or basic statistics. Then a crucial step arrived: the advent of Deep Learning and neural networks.
Large Language Models stem from this revolution. They are called “large” because they boast tens or even hundreds of billions of parameters. A parameter is somewhat like the “position of a tiny component” in the hive’s complex organization. Each parameter “learns” to weight or adjust a signal to better predict the next token in a given sequence.
1.2. A hive built on massive amounts of data
To build their hive, LLMs need a huge amount of “pollen”: text. They ingest phenomenal volumes of content, from digitized books to press articles, forums, and social media. By absorbing all that data, the model’s internal structure becomes shaped to capture and reflect language regularities.
Hence, these artificial bees ultimately learn that, in a given context, certain words are more likely to appear than others. They do not memorize text line by line; instead, they learn how to “statistically reproduce” typical forms, syntax, and associations of ideas found in language.
2. Stepping into the hive: an overview of how it works
2.1. Tokenization: gathering pollen piece by piece
The first step is tokenization. We take the raw text and break it into tokens. Imagine a field of flowers: each flower is like a word (or part of a word), from which a bee collects pollen. A “token” can be a whole word (“house”), a fragment (“hou-”, “-se”), or sometimes just a punctuation mark.
This segmentation depends on a vocabulary specific to the model: the larger the vocabulary, the finer the segmentation can be. Tokenization is crucial because the model then manipulates tokens rather than raw text. It is akin to the bee collecting precisely the pollen rather than taking the whole flower.
2.2. Embeddings: turning pollen into vectors
Once the pollen is gathered, it must be converted into a format the model can use: that step is called embedding. Each token is transformed into a vector (a list of numbers) encoding semantic and contextual information.
Think of it as the “color” or “flavor” of the pollen: two words with similar meanings will have similar vectors, just like two related flowers produce similar pollen. This step is essential, as neural networks only understand numbers.
2.3. The “Transformers” layers: the bee dance
In a hive, bees communicate through a “bee dance,” a complex choreography that indicates where the richest pollen is located. In an LLM, coordination is achieved via the attention mechanism (the famous “Attention is all you need” introduced in 2017).
Each Transformer layer applies Self-Attention: for every token, the model calculates its relevance to all other tokens in the sequence. It’s a simultaneous exchange of information, much like every bee saying, “Here’s the pollen type I have; what do you need?”
By stacking multiple Transformer layers, the model can capture complex relationships: it can learn that, in a certain sentence, the word “queen” refers to a concept linked to “bees” or “hive,” rather than “monarchy,” depending on the context.
2.4. Honey production: predicting the next token
Finally, the hive produces honey, i.e., the generated text. After analyzing the context, the model must answer a simple question: “What is the most likely next token?” This prediction relies on the network’s adjusted weights.
Depending on the hyperparameters (temperature, top-k, top-p, etc.), the process can be more random or more deterministic. A low temperature is like a very disciplined bee producing a predictable honey. A high temperature is like a more eccentric bee that can roam more freely and come up with more creative honey, at the risk of being inconsistent.
3. Honey in all shapes: use cases for LLMs
3.1. Assisted writing and content generation
One of the most popular uses is automatic text generation. Need a blog post? A video script? A bedtime story? LLMs can produce surprisingly fluent text. You can even steer the writing style: humorous, formal, poetic, and so forth.
Still, you must check the quality of the honey produced. Sometimes, the swarm can collect the wrong information, leading to “hallucinations”—the bee invents flowers that don’t exist!
3.2. Conversation tools and chatbots
Chatbots powered by LLMs have gained attention thanks to their more natural-sounding conversation. Picture a swarm that, upon receiving your request, flies from flower to flower (token to token) to deliver a fitting response.
These chatbots can be used for:
- Customer service
- Assistance (text or voice)
- Training and interactive tutoring
- Language learning
3.3. Automatic translation
Having absorbed texts in many languages, LLMs often know how to switch from one language to another. Many languages share grammatical structures, enabling the artificial bee to recognize them and offer translations. Results are not always perfect, but frequently surpass the quality of older rule-based systems.
3.4. Programming assistance
Some LLMs, such as those behind certain “copilot” systems for coding, can suggest correct code, propose solutions, and fix errors. This usage is increasingly popular, proving that “programming languages” are just another form of textual language in the big hive of content.
3.5. Document analysis and structuring
Besides generating text, LLMs can also summarize, analyze, label (classify), or even extract insights from text. This is quite handy for sorting large volumes of documents, gathering customer feedback, analyzing reviews, etc.