Large Language Model (LLM)

What is a Large Language Model (LLM)?

A large language model is a neural network trained to predict the next piece of text given everything before it. Trained on a vast collection of books, websites, code and other writing, it picks up grammar, facts, reasoning patterns and styles along the way. When you send it a prompt, it generates a response one token, roughly a word fragment, at a time.

That simple mechanism turns out to be very general. The same model can draft an email, summarize a contract, translate a page, classify a support ticket, extract fields from an invoice, or write and explain code, depending on the instructions it receives. This is why LLMs sit underneath most generative AI tools.

Businesses usually access LLMs through an API from a model provider, or run open-weight models on their own infrastructure when data control matters. The model is a component. The product or workflow around it decides what it sees, what it is allowed to do and how its answers are checked.

What can an LLM do well, and where does it struggle?

LLMs are strong at working with language: reading messy input, following instructions, rewriting for a different audience, pulling structure out of unstructured text and holding a natural conversation. They are also good at code and at combining information you place in the prompt.

They struggle with exact facts they were not given, events after their training, precise arithmetic over large tables, and tasks where every single answer must be correct. They can produce confident but wrong statements, so answers that matter should be grounded in your own sources and checked.

A useful rule of thumb: an LLM is excellent at turning information into language and language into structure, and much weaker as a source of truth on its own.

How do businesses give an LLM their own knowledge?

An LLM does not know your products, prices or policies out of the box. There are three common ways to give it that knowledge:

• Put it in the prompt: include the relevant facts directly, within the limits of the model's context window.
• Retrieve it on demand: search your documents for the passages relevant to each question and pass them in, known as retrieval-augmented generation.
• Train it in: adjust the model on your examples through fine-tuning, which suits tone, format and narrow tasks more than changing facts.

Most business systems rely on retrieval, because facts change and retrieval keeps each answer traceable to a source. Many combine the first two: fixed instructions and key facts in the prompt, plus retrieved passages for each question.

How do you choose an LLM?

Start from the task, not a leaderboard. Build a small set of real examples with the answers you expect, then try a few models on them. Compare quality on your examples, speed, cost per request, how much input each model accepts, and how well it follows the output formats your system depends on.

Then look at operating questions. Where is data processed and stored, and is it used for training? Is the provider's service stable enough for a customer-facing feature? Can you switch models later without rewriting the product?

Many teams use more than one model: a larger one for hard reasoning and a smaller, cheaper one for high-volume tasks such as classification. Keeping the model behind a thin layer in your code makes that mix easy to change as better options appear.

What are the risks of building on an LLM?

The first risk is wrong answers delivered with confidence. Ground responses in trusted data, show sources where possible, and keep a person reviewing anything with legal, financial or customer impact.

The second is data exposure. Prompts can contain customer records, contracts or internal plans, so check what the provider does with that data and strip out what the model does not need. Input from outside users can also try to override your instructions, known as prompt injection, which matters most when the model can call tools or see private data.

The third is change. Providers update and retire models, and behavior can shift between versions. A test set of real examples, run before every model or prompt change, is the simplest way to catch regressions before users do.

How does Vizio AI build with LLMs?

Our Venture Studio builds products and AI-enabled product features on top of LLMs. We start with product strategy and UX to define where language understanding actually helps the user, then handle the AI integration and software development: model choice, prompts, retrieval of the right context, structured outputs and the tests that keep quality in check as models change. We design the SaaS platform architecture so the model can be swapped without rebuilding the product. For new ideas, we run idea validation and an MVP first, so you learn whether users value the feature before a larger build.

A first conversation usually starts with the user problem and a handful of real examples to test against.