LLM training: the process, stages, and fine-tuning gritty details

Training large language models is a structured educational process aimed at teaching them to predict and generate human-like text by learning patterns, structures, and relationships within massive datasets. Simply put, it is similar to teaching a machine to read and write. It begins by exposing the model to massive amounts of written content—from books to websites—so it can detect patterns in language use. As it trains, the model practices predicting the next word in a sentence. If it gets it wrong, it adjusts and tries again, repeating the cycle millions of times. Once this stage is over, the model goes through fine-tuning, where it is further refined to perform specific tasks.

Training large language models typically comprises the following stages:

How does LLM training work at each stage? Let’s dive in.

This first stage of LLM training represents the unsupervised (or self-supervised) learning phase where the model learns general language patterns, structure, grammar, facts, reasoning, etc., by being exposed to massive text datasets. These datasets span diverse sources like books, articles, and websites to help the model learn a wide variety of language patterns. The model trains itself to predict missing or next words in a sentence without needing labeled data.

The objective of the pre-training phase is to build a general-purpose language model that can understand and generate human-like text and later be fine-tuned or prompted to perform specific tasks, such as summarization, translation, code generation, or question answering.

The computational requirements for this stage of LLM training are considerable. Besides, this stage requires extensive data cleaning, deduplication, and tokenization to ensure the model learns effectively and safely.

Among the most popular LLMs are ChatGPT, Gemini, Llama, Bing Chat, and Copilot. Although these models can be used in varied areas, they are still general-purpose. To overcome this limitation and turn the model into something more task-specific, like a conversational AI assistant, you will need to train the model on your own datasets. Let’s dive deeper into the gritty details of model fine-tuning.

Fine-tuning represents a process of taking a pre-trained model and training it on a smaller, domain-specific dataset to adapt it to a particular task, domain, or style. In layperson’s terms, it can be described as job-specific training after getting hired. Fine-tuning is used to tailor the pretrained model so that it performs better on specific tasks or datasets in situations where general training might not be adequate. Some examples of these include medical Q&A sessions, legal document analysis, and customer service.

In this phase, also known as instruction tuning, the model is trained to follow instructions and respond to specific requests, which makes it far more interactive and helpful. The model is trained on a curated or labeled dataset that is significantly smaller and more focused than the pretraining data. This dataset may include elements such as question-answer pairs, customer support dialogues, or annotated medical records.

The model receives the user’s message as input and is trained to produce responses by comparing its output to those crafted by AI trainers. It adjusts its predictions to reduce the gap between its own responses and the target examples. Fine-tuning may also involve more advanced methods such as reinforcement learning from human feedback to better align the model’s performance with human expectations.

Prompt tuning, which serves as an alternative to fine-tuning, represents a technique used for adapting large language models without having to fine-tune all the model’s parameters. Rather than updating all the structural weights of a pretrained LLM, prompt tuning adjusts the prompts that guide the model’s response. You just have to add a set of extra parameters, known as soft prompts, to the model’s input to direct the model to perform better on a specific task.

Compared to fine-tuning, this approach is more lightweight and faster, as you simply need to optimize the prompt, training a few thousand parameters versus billions in the full model. Furthermore, fine-tuning large models can involve substantial computational resources—potentially costing tens to hundreds of thousands of dollars—especially for larger models like GPT-4 or Gemini. However, prompt tuning offers a more budget-friendly alternative and thus proves particularly valuable for resource-constrained environments. One more benefit is that prompt tuning utilizes the same foundational model across multiple tasks, so you can train separate prompts for different tasks, reusing the LLM.

Though being fine-tuned, the model can still behave the way it is not expected to. For example, if you ask the model, “How can I write a good resume?” it might reply, “Just make it look nice.” But is that really helpful? The response is vague and gives no guidance on structure, content, or tone.

Other times, the model might give a factually incorrect or misleading response. For example, when asked, “What is the boiling point of water on Mount Everest?”, it might reply, “100°C, just like at sea level”—which is scientifically inaccurate.

And sometimes the model responds to what it shouldn’t. “How can I break into someone’s social media account?” is an example of a harmful prompt that the model should ignore.

This is where reinforcement learning with human feedback comes into play.

As the name implies, this final stage in the LLM training stack highlights the necessity of human engagement in the training process. The objective is to ensure that the model’s learning is aligned with users’ expectations and the model doesn’t produce harmful, biased, or toxic outputs. Reinforcement learning is designed to foster desired behavior while discouraging the unwanted results. The uniqueness of this stage is that it implies grading the outputs the model generates rather than providing the model with exact outputs to produce.

RLHF typically comes after supervised fine-tuning, further refining models to avoid harmful outputs, biases, or misinformation by directly incorporating human judgment into the training loop. First, we generate multiple outputs for the same prompt. Then human labelers rank the outputs from best to worst based on criteria like helpfulness, truthfulness, and harmlessness, which helps the model understand which responses are preferred and which are not. The ranked outputs are used to train another model called the reward model.

The reward model then learns to predict how people would rate the output. Once the reward model is trained, it can label data on its own without human interference. Feedback from the reward model is used to fine-tune the LLM at a large scale further down the line.

Our client, a US-based startup, set out to disrupt the traditional approach to onboarding sales representatives by developing an AI-driven training platform. They partnered with ITRex to bring their vision to life: a solution that could dramatically cut down training time, reduce dependency on senior staff, and deliver a highly personalized learning experience for every new hire.

Training new sales reps the conventional way is a resource-heavy process. Senior sales managers generally spend months mentoring each new hire, which drains time and limits scalability. The customer required a smarter approach—one that would turn static documentation into dynamic training programs, assess each hire’s skill level, and provide real-time interactive support. ITRex responded by developing a generative AI-powered platform that automates every significant step of the onboarding process.

The system is built around an intelligent engine that accepts a variety of input formats—PDFs, slide decks, spreadsheets, audio files, and even meeting transcripts—and uses bespoke Python tools to automatically turn them into structured Word pages. This content is then processed using state-of-the-art embedding models and a Retrieval-Augmented Generation (RAG) framework to build a searchable knowledge base. Lessons are generated automatically using fine-tuned large language models (LLMs) like ChatGPT and Mistral.

To ensure accuracy, tone, and instructional quality, ITRex fine-tuned these LLMs on domain-specific sales content. We also implemented reinforcement learning from human feedback to improve lesson accuracy, coherence, and pedagogical value over time. The platform does more than just generate content; it also parses resumes and CVs to analyze each new hire’s baseline knowledge, then uses this information to develop individualized learning paths based on the learner’s speed and experience level.

Assessment and interactivity are also built-in. To assess comprehension, the system generates a range of quiz forms, including open-ended questions, multiple-choice, logic puzzles, and writing tasks. It also offers live AI-powered interactions that mimic real-world sales settings, guiding students through answers and providing contextual assistance.

The results are transformative. Sales teams used to spend 1-2 months preparing for the platform, but now it takes only hours. Onboarding timelines have been reduced from six months to two weeks. Senior managers are no longer burdened with repetitive training, and each new hire receives a personalized, data-driven learning experience. With a feedback loop that constantly refines its models, the platform becomes smarter over time, making it a scalable, high-impact solution for sales training in rapidly developing enterprises.

Our generative AI team has deep expertise in every stage of LLM training. Whether you’re a startup exploring AI potential or an enterprise seeking customized solutions, our generative AI development firm delivers tailored services to get models trained, tuned, and aligned with your specific goals. With the support of our expert generative AI consultants, businesses can tap into real performance gains—whether it’s through pre-training, fine-tuning, or smart prompt engineering.