In brief
In text generation, a language model generates text based on input. This may be new text or a reworking of existing information: shorter, clearer, in a different language or in an agreed structure. The result is a proposal generated by the model. It is not an automatic confirmation that names, figures, events and conclusions are correct. The level of verification required depends on the intended use.
How does a language model construct text?
The input is converted into tokens: small chunks of text. The model calculates which subsequent tokens fit the prompt and the preceding context. This creates a response step by step. Settings can influence the selection and maximum length. This helps to guide the output, but changing a setting does not automatically make the model better informed. Missing facts must be derived from reliable data, not from a more creative answer.
What information should you include?
A good brief describes the objective, target audience, tone and desired format. For company information, the correct sources are also required. Think of a product list, procedure or relevant passage from a document. A search step can be used to retrieve that information in a targeted manner. It is often unnecessary to send a complete archive. Select only what the task requires and make it clear which information the model must not fill in if it is missing.
A practical example
An employee wants a draft reply to a customer enquiry. The software retrieves the relevant order and delivery details. The model receives only that data and drafts a short email in the desired tone. An employee checks the draft and sends it. Fixed details, such as the order number and amount, can be retrieved directly from the system. This means the language model does not need to invent facts that are already recorded in the accounts.
From ad-hoc text to a fixed workflow
Text generation may form part of a larger application. For example, you could use separate steps for summarising, classifying and drafting a response. Structured output simplifies further processing, but the software still needs to check that structure. Missing fields, invalid values and extra text may occur. You should therefore clearly distinguish between a generated draft and a final, approved document.
How do you assess quality and costs?
Test with real-world types of questions and check facts, completeness, tone and corrections. Also take into account the length of input and output: a lot of context or multiple attempts can result in additional computational work and API costs. A smaller model with targeted information may be sufficient for a well-defined task. The aim is to produce a usable text at a reasonable cost, not the longest answer or the largest number of tokens.
From concept to practice
How AITJE applies this
With āAITJE Assistentā, your team can have texts prepared, rewritten and summarised using a suitable language model. Where necessary, information from your own documents is incorporated. We configure the environment around recognisable tasks, desired response formats and user access. For business information, we also assess how a model responds when data is missing. The generated text remains a proposal that is checked in accordance with your own working procedures.
Visit AITJE AssistentFrom explanation to application
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