AI can extract information from a document, classify a customer request or prepare a response. Its practical value comes when that result fits your team\u2019s workflow. A standalone text box helps little if someone still needs to find, copy and check the data afterward. Start with a task, the necessary sources and the next step you want to improve.<\/p>\n
Use ordinary software for unambiguous decisions: required fields, payment status, access rights and comparing an amount with an agreed limit. AI can help where language or variation matters, such as summarising an email or recognising a document type. The design defines the model\u2019s task and the checks that remain outside it. A model does not need to guess an order status when you can retrieve it directly.<\/p>\n
A store receives a question about a delayed delivery. The integration retrieves the order and available shipping status. AI suggests a short summary and draft response based only on the supplied data and service guidelines. A team member sees the sources, reviews the suggestion and chooses the next step. This is a design example; available data and agreements determine what is possible in your situation.<\/p>\n
If shipping status is missing, the suggestion should say so. The system must not invent a delivery date. A refund, order change or commitment to the customer is a separate action with its own permissions and checks. Generating a draft and performing an action therefore do not need to share the same authorisation.<\/p>\n
Define which data the selected task needs and which system provides it. Respect access rights when retrieving information too. Agree which provider performs the processing and which storage and usage arrangements apply. Exporting all customer data is not a useful starting point for a focused summary.<\/p>\n
Treat text from emails, documents and websites as content, not instructions allowed to control the application. OWASP describes how prompt injection can influence model behaviour. Limit available actions, validate output in ordinary software and have a team member review relevant decisions. A single prompt instruction cannot replace that design.<\/p>\n
Create a small, representative test set with ordinary questions, incomplete information, conflicting sources and unusual documents. Assess whether the summary is accurate, the suggestion uses the right sources and an exception reaches the right person. Define unacceptable errors and when the application falls back to the existing workflow.<\/p>\n
A quickly generated answer does not automatically save time. Also measure required corrections, rejected suggestions and time spent retrieving, reviewing and following up. Compare the same kinds of tasks before and after the pilot. Use the results to improve sources, the interface or task allocation; do not publish savings you have not measured.<\/p>\n
Agree who tests changes to the model, sources and business rules. If a provider or integration is unavailable, the team needs a clear way to continue. Start with one scoped task and expand only when both the result and follow-up are useful.<\/p>\n
Read the primary guidance from OWASP on prompt injection<\/a>. Explore our automation and AI integrations<\/a> or discuss a specific workflow<\/a>.<\/p>","protected":false},"excerpt":{"rendered":" Design an AI workflow with clear sources, checks, approval and fallback. Start with one task and measure the quality of the whole process.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-969","post","type-post","status-publish","format-standard","hentry","category-maatwerksoftware"],"yoast_head":"\n