ChatGPT can help people draft, summarize and explore information. A useful business application starts by defining the task and the person responsible for checking the result. “Draft an answer from these approved support documents” is a clearer starting point than “automate customer service.”
Context is different from training
Giving a model instructions, examples or retrieved documents supplies context for its response. Training changes the model itself; supplying context gives it information to use for a particular response.
For a document assistant, an application can retrieve relevant material and supply it with the question. It must evaluate both whether retrieval found the right information and whether the generated answer uses it correctly. Relevant sources do not guarantee a correct answer. See OpenAI’s retrieval documentation and guidance on improving LLM accuracy.
Support drafts need a boundary
A support workflow might suggest a reply for an agent to review. Decide which documents the system can use, how it handles missing or contradictory information, and which requests must go to a person. A model should not invent a refund policy or promise an action the application cannot perform.
The application should enforce identity, permissions and approvals for record changes and sending. A reviewer needs to authorize any refund or customer message before the application acts.
Summaries and analysis need checking
AI can help extract and organize information, but fluency is not evidence that a number is correct. Keep the original source available, check important values and use appropriate calculation tools for arithmetic. For reporting, distinguish a recorded event from an inference about what happened next.
Our CareOps example shows that distinction: repository activity can support a description of code changes, but it does not by itself establish deployment. A staff reviewer retains responsibility for publication.
Start with a limited, measurable pilot
Choose approved or synthetic examples before using sensitive records. Define the expected output, the errors that block use, the review process and the fallback if the provider fails. Measure the complete workflow, including preparation and corrections, to assess whether it saves time.
Provider capabilities, account features, data terms and model availability change. Confirm them for the account and implementation you plan to use.
Use our AI pilot-to-production guide and evaluation worksheet to prepare the decision, or discuss an AI engineering project with RapidIteration.