Training Large Language Models

Imagine you want an AI assistant that can understand and respond to questions about financial reports, legal documents, or even customer feedback. Before it can specialize in any one domain, it first needs to learn how language works in general. This is where the “pre-training” phase comes in. During pre-training, a Large Language Model reads vast amounts of text drawn from books, websites, research papers, and other diverse sources. The result is a foundational understanding of grammar, sentence structure, and contextual cues.

One common strategy to achieve this is known as Masked Language Modeling (MLM). Think of it like giving the model a sentence with some words hidden and asking it to fill in the blanks. For instance, if it sees a sentence like “The CEO announced the [MASK] results,” it must guess the right word—“financial,” “annual,” or something else that fits logically. By solving puzzles like this at scale, it becomes proficient in predicting what words should go where.

Another approach, called Causal Language Modeling (CLM), tasks the AI with predicting the next word based only on what it has already seen. For a phrase like “Sales revenue grew by 20% and profits…,” the model learns to predict a sensible continuation, such as “increased,” “rose,” or “climbed.”

These methods teach an LLM how language flows, allowing it to store factual knowledge it encounters along the way. For a specialized business application—like analyzing contracts or responding to customer service inquiries—it’s crucial to have a strong linguistic backbone. Pre-training provides exactly that: a well-rounded “language brain” ready for more focused training.

Once the model has a broad language foundation, organizations refine it for specific tasks through a process called fine-tuning. This means exposing the model to a narrower dataset filled with specialized information relevant to a particular domain or use case.



In a real-world scenario, a retail company might fine-tune an LLM on its product descriptions, FAQs, and customer feedback, enabling the AI to handle online shopping queries with exceptional accuracy. A law firm could feed the model legal briefs and case law references to help draft and review documents more quickly. Meanwhile, a hospital might train the LLM on medical texts, ensuring it can understand and even summarize patient records or clinical research papers without confusing medical jargon.

Because the model has already absorbed the nuances of language during pre-training, fine-tuning doesn’t require billions of lines of data. Instead, it only needs a well-curated dataset from the specific field. The result is a highly specialized AI system that can perform targeted tasks—such as classifying sentiment in customer reviews or accurately answering questions about corporate policies—with greater reliability and relevance.

Training a single LLM can involve millions or even billions of parameters—akin to having millions or billions of tiny dials that need to be adjusted to make the model’s predictions accurate. This demands powerful hardware and parallel computing strategies, especially in enterprise settings where time to market can be a critical factor.

Companies looking to shorten training time or handle extremely large models rely on distributed training, in which the computational load is shared across multiple machines or graphics processing units (GPUs). In more practical terms, this is like taking a huge task and dividing it among many teams working simultaneously.

One way to do this is by splitting the training data and sending different slices to different machines. Another is by breaking the model into distinct sections—say, the early part of the model on one GPU and the later stages on another—so that each hardware unit tackles a specific segment. A further strategy is to organize the work as a pipeline, allowing one batch of data to be processed while another is queued up, minimizing downtime.

For a global bank trying to analyze massive transaction data, or a media conglomerate training a model on petabytes of text and subtitles, these distributed techniques make it possible to handle the workload in a practical timeframe. Without them, the model might still train effectively, but it could take months instead of weeks or days.

Even a powerful and specialized model can produce answers that feel off-base or reflect unintended biases. This is where Reinforcement Learning from Human Feedback (RLHF) comes into play. The idea is to give real people a chance to guide the model, ensuring it aligns more closely with human values, ethical considerations, and practical expectations.

Companies might employ RLHF by first letting human annotators review the AI’s responses. These annotations help the system understand what “good” answers look like, whether it’s a respectful tone, a correct fact, or a response that complies with regulatory guidelines. A “reward” mechanism is then introduced, nudging the AI toward behaviors that human reviewers prefer. This can be especially relevant for customer-facing applications—like chatbots—where tact, empathy, and clarity matter just as much as correctness.

A global healthcare provider, for instance, may require the AI to prioritize patient privacy and clarity. RLHF can ensure that any automated patient-facing communication is not just clinically correct but also compassionate and easy to understand.