What is a domain-specific LLM?

Artificial intelligence (AI) models can be trained or fine-tuned on domains such as the legal field, finance and cybersecurity. These models better understand the linguistic characteristics of a particular domain, with greater expertise than a general language model.

Because domain-specific AI models understand industry-specific technical jargon, formatting conventions and contextual nuances, they provide high-quality outputs that are more relevant and precise for that domain. This helps make models more safe and trustworthy, which is especially useful in areas that are governed by strict regulatory and compliance frameworks.

PubMedGPT, for example, is an LLM fine-tuned on healthcare literature from the National Institutes of Health’s PubMed database. Its training data includes scientific abstracts, research articles and medical terminology, which gives the model the ability to perform clinical decision support, research summarization or answering medically relevant queries with greater accuracy.

The field of natural language processing (NLP) has given the world many powerful general-purpose LLMs, such as OpenAI’s GPT-5 (the LLM powering ChatGPT), or Meta’s LLaMA, which are trained on broad, mixed-domain datasets. Unlike these generic LLMs, domain-specific models are tailored to a specific field through specialization and adaptation techniques. Here are a few of the most common techniques to give an LLM domain-specific knowledge:

• Prompt engineering

• Training from scratch

• Retrieval augmented generation (RAG)

• Fine-tuning

• Hybrid approach

The easiest and fastest way to get domain-specific knowledge from a general-purpose LLM is through prompt engineering, which does not require additional training. Users can modify prompts in all sorts of ways. For example, a prompt like “answer in the voice of a trained legal professional” might provide answers that are more useful to a user looking to recreate “legalese” (Note that LLMs are not recommended to be used for legal advice!).

Prompt engineering is helpful in scenarios where time is of the essence. With no additional training, data or computational resources required, prompt engineering requires a minimal amount of human labor to craft the prompts.

In contrast, training a model from scratch on domain-specific data is probably the most arduous option. This option provides maximum control and customization but is comparatively very resource-intensive in terms of the compute, data and engineering talent required. A practitioner could, for example, train a model from the ground up on extensive legal corpora. BloombergGPT is a 50-billion parameter generative AI model which was purpose-built from scratch using one of the largest domain-specific datasets—363 billion tokens of financial data.¹

Retrieval augmented generation (RAG) is an architecture for optimizing model performance by connecting a model with an external knowledge base. Practitioners can augment existing, widely available foundational models with specialized knowledge via RAG. A user submits a query, the model retrieves data from the external source and provides an answer by blending its training with the retrieved data. Continuing the legal industry example, a practitioner could simply connect an existing LLM to a collection of recent real-world case law, which would allow the LLM to use its extant reasoning capabilities to integrate the new information into its outputs.

RAG is more cost-efficient and faster than training a model from scratch, especially when working with massive LLMs, where further training risks damaging general abilities. However, it does introduce latency from the LLM taking time to retrieve additional data.

Where RAG augments a model by connecting it to external data sources, fine-tuning optimizes a pretrained model through re-training for domain-specific tasks. Fine-tuning leverages the language understanding gained through its initial training process and then adapts it for more specialized use cases. This process is much also less expensive than training from scratch, especially when working with smaller models or when knowledge requirements are relatively fixed.

One method of fine-tuning LLMs might involve a domain-specific tokenizer. General-purpose models struggle with specialized jargon, abbreviations and terms unique to a domain. Tokenizers break text into smaller units (tokens) that the model can process. For example, in genomics, a standard tokenizer might split the gene "BRCA1" into "B," "R," "CA," and "1," losing the meaning of the gene name. A domain-specific tokenizer would treat "BRCA1" as a single token, preserving its context and meaning.

Pretrained embeddings can also be updated with fine-tuning by adjusting the vector representations so domain-specific words and phrases are situated more meaningfully in semantic space. The tokenizer stays the same, so the way text is split into tokens doesn’t change. For example, in law, the term “court” would be clustered closely with other legal terms, less closely with “basketball” or “tennis.”

Transfer learning is distinct from fine-tuning. While both reuse preexisting machine learning models as opposed to training new custom models, transfer learning allows a pre-trained model to apply its knowledge to an entirely new task. Where fine-tuning involves updating the pre-trained model’s weights, transfer learning might only involve freezing the original model’s layers or adding a new task-specific layer. MedPaLM is an example of a domain-specific-LLM that was built on top of PaLM, a general-purpose LLM, using transfer learning.

Let’s say a large law firm wants an AI agent to draft legal documents that sound exactly like their in-house attorneys and always cite the most recent case law. A hybrid approach might involve using fine-tuning to optimize for style and reasoning. The firm collects thousands of memos, e-mails and briefs, capturing the firm’s voice and tone. They re-train an LLM on this new dataset, and the LLM learns not only this style, but also picks up the reasoning steps and output structure their lawyers demonstrate in these communications across common legal applications.

Then, they use RAG to connect the newly specialized LLM to a database containing the latest statutes, regulations and case summaries. The model can now query this information in real time to retrieve the most relevant and recent information.

The result is an AI chatbot powered by a domain-specific large language model that produces professionally formatted, on-brand legal memos that sound like the firm’s lawyers, while including the most current and accurate legal information. This chatbot can then be used to automate domain-specific workflows, like legal question-answering and decision-making.

The stakes can be high in specialized domains. An AI hallucination could result in catastrophic outcomes. Once an LLM has been developed, practitioners can use established benchmarks to measure the success of the model’s adaptation to a specific field. Task-specific benchmarks, adversarial prompting, and fact-checking against trusted sources are examples of techniques that can help optimize models for accuracy, domain knowledge alignment, robustness, safety and performance on specific tasks.