What is AI text generation?
AI text generation is the use of a large language model (LLM) to produce written content inside your product — drafting a reply to a customer email, summarising a long document, generating a product description from a set of attributes, or producing a weekly report from raw data. The user provides input (or the system provides context), and the AI produces polished text output.
This is different from building a chatbot. A chatbot is a conversational interface where the user exchanges messages with an AI. Text generation is typically embedded into a specific workflow — you click "draft a reply", the AI writes something, you edit and send. The AI is a writing accelerator, not a conversation partner.
The practical enabler is the API access now offered by OpenAI (GPT-4), Anthropic (Claude), and Google (Gemini). Your application sends a prompt to the API and receives generated text in response, without needing to host or train a model.
When does your app need it?
- Your users spend significant time writing similar content repeatedly — proposals, emails, descriptions, reports — and would benefit from a first draft
- You have structured data (a form, a record, a dataset) that needs to be translated into readable prose
- You want to offer a summarisation feature for long documents, threads, or records
- Your platform serves content creators, marketers, or copywriters who want AI assistance embedded in their workflow
- You need to generate personalised content at scale — individual emails, product recommendations, tailored reports — that would be impractical to write manually
- You want to give your product a consistent brand voice by defining a system prompt that shapes all AI output
How much does it cost?
Adding AI text generation typically adds 8–16 hours of development — roughly $1,000–$4,000 AUD.
At the simpler end, this is a single generation endpoint — a prompt template, an API call, and a streaming text response displayed in the UI. At the more complex end, it includes multiple generation types, careful prompt engineering for each use case, rate limiting and cost controls, user feedback mechanisms to improve output quality, and guardrails to prevent off-brand or inappropriate output.
How it's typically built
Your application constructs a prompt that combines a system instruction (the persona and rules the AI should follow) with the user's context (the data relevant to this generation task). This is sent to the LLM API, and the response is streamed back to the client so users see text appearing progressively rather than waiting for the full response.
Prompt engineering is a meaningful part of the build — the quality of output is directly dependent on how well the prompt is crafted. System prompts define tone, format, length constraints, and what the AI should or should not include. For applications where the AI is writing on behalf of a business, establishing the right persona and guardrails is critical. Cost management is also important: different models have different per-token pricing, and a poorly scoped prompt can generate expensive API calls at scale. Rate limiting, caching common generations, and choosing the right model tier (not every use case needs the most expensive model) are all part of a production-grade integration.
Questions to ask your developer
- Which model and provider will you use? OpenAI, Anthropic, and Google each have different strengths, pricing, and terms around data handling — relevant if you are processing sensitive customer information.
- Will responses be streamed or generated in full? Streaming improves perceived performance significantly for longer outputs.
- How will you control costs as usage scales? Per-token costs can become significant; discuss rate limits and usage monitoring.
- What happens if the AI generates something inappropriate or wrong? You need a feedback mechanism and a content policy before going to production.
- Does generated content need to be stored? If users want to retrieve previously generated drafts, you need to store API responses, which adds to data architecture.
See also: AI-powered search · AI document extraction · App cost calculator