What is LLM training?

Large language models (LLMs) have become foundational to modern AI systems, powering applications such as chatbots, code assistants, search and enterprise knowledge tools. Interfaces like ChatGPT built on models like GPT-4 show how LLMs support real-time interaction, summarization and reasoning across a wide range of natural language processing (NLP) tasks. 

They are typically built on transformer architectures, a deep learning design that enables models to process long‑range language patterns. As systems trained on vast amounts of text, they can model patterns, semantics and reasoning in language. While many teams interact with LLMs through APIs, few understand how to actually train one. Large-scale data, statistical learning algorithms and iterative refinement are all combined in the structured, multi-phase process of LLM training.

Training typically begins with a pretrained model or base model that already has broad language knowledge, which is then refined for more specific tasks. LLM training is divided into three phases: pre-training, fine-tuning and post-training.

The initial stage of LLM development is pre-training. This step uses self-supervised learning to train the model on massive text collections, such as web pages, books, articles, and source code. During training, the text is first divided into tokens through tokenization. Tokens are the fundamental textual units often smaller than complete words. 

After tokenization, each token is encoded into numerical representations that the model can process. As a result, the model can handle multiple languages, writing styles, and formats. In this step, given a sequence of tokens, the model must predict the next token. 

Through many training iterations, the model develops a broad understanding of language without relying on manually labeled data. It learns syntax, semantics, stylistic patterns, and implicit world knowledge encoded in the training data. This learning happens only during training as the model does not continue learning during inference time.

The primary challenge in pre-training is scalability. Modern training runs process trillions of tokens and rely on large, distributed GPU clusters. As a result, concerns such as memory efficiency, data throughput, multiprocessing workflows and distributed strategies become central challenges. However, pre-training is not simply about scale. Data quality, diversity, and filtering matter as much as raw volume. Deduplication, removal of low-quality content, and mitigation of bias are critical steps during dataset construction.

Pre-training is also resource intensive as it requires extensive computational infrastructure, long training times, and careful optimization. Hence, instead of creating their own models, most teams depend on pretrained models or open source models such as IBM Granite® and LLaMA. The output of this phase is a general-purpose language model but not yet specialized for particular use cases.

Once a model is pretrained, it can be fine-tuned for a specific domain or task. Fine-tuning LLMs adapts them to unique use cases such as customer service queries, specific domain vocabulary, coding tasks, or enterprise- specific knowledge. Instead of learning language from scratch, the model modifies its existing parameters through small but high-quality training datasets as it already understands general language and helps achieve specialization at a lower cost.

There are various approaches for fine-tuning. Once trained, a model can be fine‑tuned for a specific domain or task. Fine‑tuning LLMs allows them to adapt to domain‑specific knowledge. Instead of learning a language from scratch, the model modifies its existing parameters with small but high-quality training datasets.

The model is trained through input/output samples in which each prompt has a clearly defined “correct” response. During training, the model learns to map specific inputs to target outputs, reinforcing patterns such as tone, structure, and task-specific behavior. SFT is typically the first step in adapting a general-purpose model to practical use cases.

Instruction tuning: Prompts are written as natural language instructions and responses show the expected behavior. It is a form of supervised fine-tuning in which training examples are given as natural language instructions to improve a model’s ability to follow user requests.

The model is fine-tuned on text data from a specific domain, such as legal, medical, or enterprise documents, helping it to better understand domain terminology, structure, and context.

Dataset preparation is critical at this stage. Poor labeling or ambiguous examples can degrade the model performance. Datasets for fine-tuning are cleaned, selected and formatted regularly. 

The way that model parameters are updated during fine-tuning might also vary:

All model weights are updated during training. This approach offers great flexibility as the model can fully adapt to new domains or behaviors but requires substantial compute and memory resources.

A parameter-efficient technique where small trainable layers are added to the model while the original weights remain unchanged. Instead of updating the full model, only these lightweight components are trained. This approach reduces memory and training costs while enabling effective specialization for downstream tasks.

By concentrating on accuracy and consistency within a specific domain, fine-tuned models can outperform general-purpose models on specific tasks. Many teams often use Hugging Face tools, Python pipelines, and frameworks such as PyTorch to manage datasets, model loading and training workflows. The model is more specialized after fine-tuning, but alignment and safety modifications might still be necessary, which brings us to the last stage of training.

Post-training focuses on aligning model behavior with human preferences, safety requirements and application-specific limitations. While pre-training teaches the model the structure of language, alignment teaches it how to respond. This stage also helps reduce hallucinations where the model generates incorrect or fabricated information. 

One widely used approach is reinforcement learning from human feedback (RLHF). In this approach, humans compare model outputs and express preferences. A reward model is trained on these preferences and directs further LLM optimization. Improvements in helpfulness, instruction-following, and safety often result from alignment rather than extended pre-training.

Alignment is not a one-time step. As models are deployed and used in real-world settings, new failure modes emerge. Post-training is hence a continuous process, tightly coupled with evaluation and feedback loops. Prompt engineering plays a complementary role by helping users shape queries for better results. Some real-world systems pair LLMs with retrieval-augmented generation (RAG), helping the model to refer to external documents for improved factual grounding.

An example of alignment through human feedback is InstructGPT, developed by OpenAI.^[1] In this work, researchers fine-tuned GPT-3 by using human-written examples and preference rankings of model outputs. Despite being smaller, human evaluators preferred the 1.3B-parameter InstructGPT over the original 175B-parameter GPT-3. This finding showed that aligning a model with human feedback can improve usefulness, truthfulness, and safety more effectively than simply increasing the model size.

Unlike standard machine learning, evaluation is required at every stage of LLM training. Some applications require real-time responses, making model efficiency critical. Common evaluation methods include checking metrics such as perplexity. They also test the model on real tasks including code generation or question answering, use validation data to detect overfitting and rely on human review to determine usefulness, safety and consistency. Assessments need to be repeated as models and data change because language competency varies so much. 

The best practices are to keep training data clean and well-filtered, improve datasets step by step, scale models gradually and address safety early. It is also important to document data and training choices so results can be understood and reproduced later.

LLM training is best understood as a multi-phase, data-centric approach rather than a single process. For most organizations, the greatest value lies not in training AI models from scratch, but in carefully adapting and aligning existing ones. As research continues to evolve, the boundaries between these phases are becoming more fluid. The underlying principle remains the same: successful LLMs are shaped as much by data, preprocessing, annotation quality, and iterative evaluation as by the architecture itself.