ChatGPT and the other GenAI chatbots that have taken the tech world by storm are all trained on massive datasets comprising trillions of data points. The algorithms that dictate how large language models (LLMs) process queries and spit out responses often involve billions of logic nodes. It’s the enormity of these training datasets and algorithms that give an LLM its power, but it seems like we might have hit the ceiling when it comes to feeding GenAI.
Bigger is no longer automatically better. Instead, it’s time to get smarter, as Sam Altman, CEO of OpenAI, acknowledged recently. “I think we’re at the end of the era where it’s going to be these, like, giant, giant models,” he said, adding, “We’ll make them better in other ways.”
Today there is arguably more momentum around specialized LLMs that are trained only on industry-specific, company-specific, and even personal data. Their training datasets and processing demands are smaller, but they are more relevant and more precise for the use cases to hand. Many enterprises are already building their own LLMs using specialized data, instead of adopting public, general LLMs.
True, the trend is also often driven by a fear of data leaks and a desire to close their LLM to hackers, but it’s also a positive choice to build a more specialist LLM. As time goes by, you can expect to see more smaller specialized LLMs, and fewer general ones, for a number of reasons.
Specialized LLMs are more trustworthy
The mass amount of data that’s fed into general LLMs makes them excellent at producing reasonably comprehensible text, but it also makes them imprecise and prone to hallucinations and bias. They are easily misled by all the irrelevant information they contain. In contrast, specialized LLMs are more likely to deliver accurate answers that aren’t fogged by irrelevancies.
Inaccurate answers aren’t just annoying. In some industries, they can have dangerous consequences. No one would trust a health LLM that gives unreliable diagnoses, for example. It’s arguable that only specialist LLMs, with extra quality and privacy mechanisms, meet compliance requirements for high-risk industries.
“Domains like healthcare and law come with heightened stakes. A misinformed output can be detrimental. Domain-specific LLMs, often embedded with additional safety mechanisms, can deliver more trustworthy insights,” said Arsalan Mosenia, AI Lead at Google.
Smaller models are more nimble
Speed is everything when it comes to enterprise use cases of GenAI, especially data science. Line-of-business users want fast answers to their queries, and data scientists are trying to clear their backlogs of questions and inquiries as quickly as possible.
Smaller LLMs can analyze an input, scan data, and deliver outputs in far less time than a giant generalized model.
“An LLM that is very specifically trained on a specific block of content will actually be smaller than a broad, generic LLM, and interestingly faster. The smaller it is, the faster it is to respond,” pointed out Avi Perez, CTO of Pyramid Analytics, a business intelligence platform that allows users to connect multiple models for different projects. “So there’s going to be a natural progression whereby if I want a faster, quicker LLM that is cheaper to run and smaller, I want it to be more narrowly focused. So we see that being an outcome in the market too.”
Giant LLMs are too expensive
Cost is another important factor. LLMs suck up huge amounts of both data and energy. Big Tech has already overextended itself when it comes to investments in giant data centers, so the idea of going even bigger is not viable. If you’re training your own model, you have to pay for the energy to run all the processes – andfor the resources needed to collect, clean, and verify the vast amounts of training data.
The exorbitant cost of training a model puts it out of reach even of many large organizations. In industries like law, you need your model to absorb a lot of dense legal documents if you want relevant, law-specific outcomes. Adding on general information would quickly exceed your resources.
However, smaller, specialized LLMs are a lot cheaper to develop, train, and run. A medium-sized business could conceivably find the resources to train a specialist model only on industry-relevant data. That way, they’d get a fast, private, and secure model that delivers the outcomes they need, without hallucinations or sky-high costs.
Enterprises need chatbots that talk their language
Moving beyond the risk of hallucinations and inaccuracies, many enterprises need an LLM that can understand and speak their jargon. A general AI bot might not understand a customer’s complaint about their insurance claim or your query about a marketing campaign, and it can’t be trained to give specific answers.
“Digging deep into, say, selecting which brand’s shoe model is right for your foot or listening to your customers describe their confusion with their bill requires highly specialized knowledge,” noted Justin Davis, cofounder and CEO of Nurdle AI. “This is not a criticism of ChatGPT or Bard in any way. It’s simply a recognition that, for specific brand AI use cases, specialized models are a requirement.”
You need an LLM that’s informed solely by industry-relevant data, and then fine-tuned to your knowledge base, brand voice, products, IP, industry terminology, customer concerns, and other nuanced considerations, so it’s aligned with your specific use case issues. It’s the only way to build a chatbot that provides personalized interactions that are based on customer behavior, preferences, and previous brand interactions.
Specialized LLMs check all the right boxes
Enormous, general-purpose LLMs have their place. They deliver comprehensible text and are great at handling the average Joe’s average query. But when it comes to enterprise use cases, specialized LLMs are ascendent. Because of their advantages in terms of speed, cost, accuracy, trustworthiness, and security, specialist LLMs are likely to draw increasing interest in 2026.