Generative AI Use Cases and Limitations
Match generative AI capabilities to practical use cases while understanding where human review remains essential.
Start with the user outcome
Choose a use case by asking what a person needs to accomplish, not by starting with a model feature. Drafting a support reply, extracting fields from invoices, or creating study questions are clearer outcomes than “add AI.” Identify who benefits, what input is available, what a good result looks like, and what happens if the output is wrong. This framing helps you decide whether generation, search, rules, or a combination is actually appropriate.
Good fits for assistance
Generative AI is often helpful for first drafts, rewriting, summarization, brainstorming, translation, code suggestions, and converting information between formats. These tasks are safer when a person can inspect the result and the source material is available. The model can reduce repetitive effort without owning the final decision. Even here, define boundaries: a summary should preserve important qualifications, a translation should preserve meaning, and generated code should be reviewed and tested.
function validateInvoice(invoice) {
const required = ['invoiceNumber', 'total'];
const missing = required.filter(field => invoice[field] == null);
return { valid: missing.length === 0, missing };
}
const extracted = { invoiceNumber: 'INV-1042', total: 1250 };
console.log(validateInvoice(extracted)); // { valid: true, missing: [] }Extraction and transformation
A model can read an email or document and return selected fields, classify a request, or turn notes into a checklist. Structured outputs and validation make these workflows more dependable. However, scanned text, unusual layouts, missing fields, and ambiguous language can cause mistakes. Preserve the original input, record confidence or validation results where possible, and send uncertain cases to a person. Never silently treat a guessed value as a confirmed fact.
Common limitation: hallucination
A hallucination is an answer that sounds plausible but is unsupported or false. It can happen because the model is predicting likely language rather than consulting a guaranteed fact store. Reduce the risk with trusted retrieval, source citations, constrained schemas, calculations in code, and explicit abstention rules. Still verify important claims independently. A citation-looking phrase is not enough; open the source and check that it really supports the statement before relying on it.
Common limitation: bias and context
Training data reflects the world’s unequal representation, stereotypes, errors, and conflicting viewpoints. A model may produce unfair descriptions, overlook a community, or apply a pattern inappropriately. It can also miss local context, cultural nuance, or a user’s actual intent. Test with varied examples, involve affected people in review, avoid sensitive decisions based only on generated output, and monitor outcomes after launch. Polite language does not prove that the result is fair.
Privacy and content rights
Inputs may contain names, contact details, confidential plans, health information, or customer records. Before using a model, know where data is sent, how long it is retained, who can access it, and whether it is used for training. Minimize and redact information that is not necessary. Generated or transformed content can also raise copyright, licensing, likeness, and attribution questions. Use approved sources and review the rights and policies relevant to your situation.
Reliability and cost trade-offs
A larger model may improve difficult tasks but increase price, response time, and operational complexity. A smaller model may be sufficient for routing, extraction, or simple rewriting. Retrieval can add current knowledge but introduces indexing and access-control work. Human review improves safety but adds time and labor. Compare the complete workflow on realistic examples, including retries, failed requests, review time, and downstream corrections rather than comparing model output in isolation.
A responsible workflow
Define the task, limit inputs, provide trustworthy context, validate outputs, and log enough information to investigate failures without storing unnecessary personal data. Add approvals for high-impact or irreversible actions, make uncertainty visible, and give users a way to report errors. Re-evaluate after model or prompt changes. The goal is not to remove people from every step; it is to use automation where it adds value while preserving accountability, dignity, safety, and meaningful human control.