Beyond chat interfaces, generative AI is used in production systems for well-defined, narrow tasks — usually with guardrails, structured output, and human review, not as an open-ended assistant.
Content & Communication
- Drafting marketing copy, product descriptions, and email templates (typically human-reviewed before publishing)
- Summarizing long documents, meeting transcripts, or support tickets
- Translating text between languages
Software Development
- Code completion and generation (e.g. in-IDE coding assistants)
- Generating unit tests from existing code
- Explaining unfamiliar code or error messages
Customer-Facing Systems
- Support chatbots that answer questions grounded in a company's documentation (see RAG)
- Search interfaces that answer natural-language questions instead of returning a list of links
Data & Documents
- Extracting structured data (names, dates, amounts) from unstructured documents like invoices or contracts — see Structured Output
- Answering questions over internal knowledge bases (PDF question-answering, document chatbots)
A Concrete Example — Support Ticket Summarization
Input (raw support ticket, 800 words of back-and-forth):
"Hi, I ordered product X on the 3rd... [long thread]..."
Prompt:
"Summarize this support ticket in 2 sentences: the customer's issue
and the current resolution status."
Output:
"Customer received a damaged product X and requested a replacement.
Replacement has been shipped; awaiting delivery confirmation."This is a narrow, well-scoped use of generative AI — a single, bounded task with a clear success criterion, not an open-ended assistant.
Practical Pattern: Narrow > General
The generative AI applications that succeed in production are almost always narrowly scoped — "summarize this ticket," "extract these five fields," "answer questions using only this document set" — rather than open-ended "do anything" assistants, which are much harder to make reliable, safe, and cost-effective.
Common Mistakes
- Deploying an open-ended chatbot for a task that would be better served by a narrow, well-tested feature
- Assuming a proof-of-concept prompt that "worked once" is production-ready without evaluation (see LLM Evaluation)
Interview Relevance
"Give an example of a good generative AI use case, and a bad one" — a strong answer contrasts a narrow, bounded task (ticket summarization) against a poorly-scoped one (an assistant expected to autonomously handle arbitrary customer requests with no guardrails).
Practice Question
Propose a generative AI feature for an e-commerce app that is narrowly scoped, has a clear success criterion, and doesn't require the model to take real actions on its own.
