What Generative AI Can and Can't Do for Your Product

Generative AI — the models behind text generation, chat, image creation, and coding assistants — is genuinely powerful for some product features and a genuine liability in others. The difference is not the technology; it is whether the feature can tolerate output that is sometimes wrong, and whether a person reviews it before it matters.

Here is a clear-eyed view of where it fits.

What Generative AI Does Well

Producing a first draft

The single most reliable use: turning a prompt into a draft a person then reviews. A summary of a long document. A first-pass reply to a customer. A product description from a few attributes. A block of routine code. The human stays in the loop, so a mediocre draft is still a time-saver and a wrong draft gets caught.

Understanding unstructured input

Pulling structure out of messy input — extracting the key terms from a contract, categorising a free-text support ticket, answering a question from a document. The model reads something a rules engine could not parse and returns something usable.

Transforming content

Translating, rephrasing for a different audience, changing format, adjusting tone. The source meaning is preserved and the transformation is low-stakes.

Handling variety

Where inputs vary too much to write rules for — every customer email is different, every uploaded document is formatted differently — generative AI copes with the variety in a way explicit logic cannot. See generative AI MVP development: what founders should know.

What Generative AI Does Badly

Being reliably correct without review

Generative models produce plausible output, not verified output. They will state a wrong fact, cite a source that does not exist, or make an arithmetic error with total confidence. Any feature where the output must be correct and no one checks it is a poor fit.

Precise calculation and factual lookup

A language model asked to add numbers or recall a specific figure is generating text that looks like an answer, not computing one. For anything numeric or factual, the model should call a real calculator or database, not produce the answer itself.

Decisions with consequences

Approving a loan, diagnosing a condition, deciding a legal outcome, setting a price that affects revenue — generative AI can assist a human making these, but it should not make them. The cost of a confident wrong decision is too high, and the reasoning is too hard to audit.

Guaranteeing consistency

The same prompt can produce different outputs. For a feature that needs to behave identically every time, this is a problem you have to design around — with fixed templates, validation, or constrained output.

Core Feature vs Helper Feature

This is the key scoping decision for an MVP:

	Helper feature	Core feature
Example	“Draft a reply” button, “summarise” option	The product is the AI output users rely on
User reviews output?	Yes, always	Often not
Risk of a wrong output	Low — user catches it	High — user acts on it
Validation needed before launch	Moderate	Extensive
Good starting point for an MVP	Yes	Only with heavy validation and clear failure handling

Most first versions are better off with generative AI as a helper — visibly a draft, clearly editable, obviously the user’s responsibility to check. Making it the core, where users trust the output directly, is a bigger commitment that needs output-quality testing and a plan for when it fails.

What to Validate Before Building

A short proof of concept should answer four things:

1. Is it good enough? Run the model on real examples from your domain — your documents, your customers’ language, your use case. Measure how often the output is usable.
2. What happens when it is wrong? Write out the failure cases. A bad summary, a wrong extraction, an off-tone reply. Is each one recoverable, annoying, or damaging?
3. Do users want it? Some people would rather write the email themselves than edit an AI draft. Test whether the generated version is actually preferred.
4. What does it cost per use? Generative AI APIs charge per request. Estimate the cost at your expected volume — a feature that is cheap at 100 users can be a problem at 100,000.

This is why an AI proof of concept often precedes the MVP when the core uncertainty is whether the AI is good enough.

Handling the Wrongness

If generative AI is in your product, plan for imperfect output from the start:

• Show it as a draft. Make it visually clear the output is generated and editable.
• Keep a human in the loop where errors are costly. The AI proposes, a person confirms.
• Constrain the output where you can — templates, allowed values, validation checks.
• Ground it in real data. For factual answers, have the model work from your actual documents rather than its training, and reduce hallucinations with retrieval.
• Let users report bad output so you can measure and improve.

The Takeaway

Generative AI is a strong fit for features that produce a reviewed draft, interpret unstructured input, transform content, or handle high variety — and a poor fit for anything that must be reliably correct without review, involves precise calculation or factual lookup, or makes a consequential decision. For an MVP, start with it as a helper feature, validate accuracy on your real data, and design for imperfect output from day one.

For building one specifically, see generative AI SaaS development: a founder’s roadmap.

Frequently Asked Questions

What is generative AI good at in a product?

Producing a useful first draft from a prompt — text summaries, replies, descriptions, code, images — and understanding or transforming unstructured input like documents and free text. It works best where the output is reviewed by a person or where an imperfect result is still useful.

What is generative AI bad at?

Anything that requires being reliably correct without review — precise calculations, factual lookups it treats as generation, decisions with legal or financial consequences, and tasks where a confident wrong answer causes harm. It also cannot guarantee the same output for the same input.

Should generative AI be a core feature or a helper feature in an MVP?

Helper features — draft this, summarise that, suggest a reply — are lower risk because the user reviews the output. Making generative AI the core of the product, where users rely on its output directly, raises the stakes on accuracy and requires much more validation before launch.

How do I validate a generative AI feature before building it?

Run the model on real examples from your domain, measure how often the output is good enough, define what happens when it is wrong, and check whether users actually want the generated version. A short proof of concept usually answers these before you commit to a full build.