Generative AI

What is Generative AI?

Generative AI refers to models that produce new output rather than only classifying or scoring existing data. A person or a program gives the model an instruction, and the model generates a response piece by piece based on patterns learned from very large training sets. Text generation is usually done by a large language model; images, audio and video use related model families.

In business, generative AI shows up in two places. Inside the company, it drafts emails and reports, summarizes calls and documents, answers staff questions about internal policies and helps developers write code. Inside products, it powers features customers use directly: a writing assistant in a SaaS tool, a support chat that answers from the help center, or a search box that understands plain-language questions.

The model itself is usually rented from a provider through an API. The work that makes it useful is everything around it: the data it can see, the instructions it gets and the checks on what it returns.

What is generative AI used for?

Generative AI earns its keep on work that involves a lot of reading and writing. Typical uses include:

• Content drafts: product descriptions, emails, social posts and proposals for a person to edit.
• Summaries: condensing calls, tickets, contracts or research into the points that matter.
• Question answering: answering customer or staff questions from a company's own documents, often with retrieval-augmented generation.
• Code: generating, explaining and reviewing software.
• Product features: smart search, in-app assistants and automatic tagging inside a software product.

The strongest uses share a pattern: the output is easy for a person or a system to check, and a wrong answer is cheap to catch before it does damage, such as a draft a salesperson edits before sending.

What are the limits of generative AI?

Generative models predict likely output, so they can produce fluent text that is wrong. This is called AI hallucination, and it is the main reason to ground answers in trusted sources and to review output before it reaches customers.

Other limits are practical. Models only know what was in their training data and what you put in the prompt, so they will not know your prices or policies unless you provide them. Output can vary between runs, which makes testing harder than with ordinary software. Cost and speed depend on how much text goes in and out. And sending customer or company data to an outside model needs a clear check of the provider's data terms.

None of this rules generative AI out. It means the feature needs the same design care as any other part of a product.

How is generative AI added to a product?

Most teams do not train their own model. They call an existing model through an API and build the product layer around it. That layer usually includes:

• a prompt that sets the task, tone and rules;
• a way to pull in the right context, such as the user's account data or relevant help articles;
• checks on the output, from format validation to AI guardrails that block unsafe or off-topic answers;
• a set of test cases that measures quality before each change ships.

Only when an off-the-shelf model with good context still falls short does it make sense to consider fine-tuning or a custom model. Each piece of this layer can be improved without changing the model, which is where much of the quality work happens.

How do you decide if a generative AI feature is worth building?

Start with the user problem, not the model. Ask where people spend time reading, writing or searching, and whether a draft or an answer would actually save them effort once they have checked it. Then test cheaply: a prototype run on real examples from your own data shows quickly whether the output is good enough, and where it fails.

Define success before building. For an internal tool, that might be time per task or how often staff accept the draft. For a product feature, it might be adoption, retention or fewer support tickets. If you cannot describe what good output looks like, it will be hard to tell whether the feature works.

Finally, compare the running cost per use with the value of the task, since every generated answer has a price.

How does Vizio AI build with generative AI?

Our Venture Studio builds AI-enabled product features and new products where generative AI is part of the core experience. We work through product strategy and UX first, so the feature solves a real user problem, then handle the software development and AI integration: prompts, context, output checks and the test set that keeps quality visible. For founders, we use idea validation and MVP development to see whether users want the feature before a full build. For existing products, we add generative features as part of product modernization and keep iterating after launch.

The usual first step is a short validation of the use case with real examples from your data.