Prompt and Knowledge-Base Development

We start with the use cases. Which tasks do you want AI to help with: answering customer questions, drafting replies, summarising calls, writing product text, assisting staff with policies? Each has different needs for accuracy, tone and risk. We rank them by value and by safety, and start with lower-risk cases. Next we gather your knowledge. We collect existing documents: price lists, brochures, FAQs, policies, process notes, email templates, past support chats and website pages. We also interview staff who answer questions daily, since much knowledge is in people’s heads. We then clean and structure it. We remove outdated or contradictory material, resolve conflicts with the owners, and rewrite content into short, self-contained entries, each covering one topic with a clear title, answer, conditions and last review date. Tables for prices and specifications are made machine-readable. We tag entries by product, audience and topic, and mark which are public and which internal. We choose the format depending on the tool: a document set for an assistant, a spreadsheet or database for a chatbot, or an indexed store for retrieval systems. Then we write prompts. A system prompt states who the assistant is, who it serves, what tone to use, what sources it may rely on, what it must never do, such as promising discounts or giving legal or medical advice, and how to respond when information is missing. Task prompts handle recurring jobs, for example “draft a reply to this enquiry using the knowledge entries” with a fixed output format. We include examples of good and bad answers, which improve consistency. We create a test set of realistic questions, including tricky ones: out-of-scope, ambiguous, abusive, and questions whose answers changed recently. We run them through the system and score accuracy, tone and safe behaviour, then refine the prompts and knowledge until results are reliable enough for the use case. For customer-facing tools we also test for attempts to make the assistant ignore its rules. We document everything and set up a maintenance process: an owner for each knowledge area, a review schedule, and a method to log wrong answers and fix their source. We train your team to update entries and adjust prompts safely. We cannot promise that an AI tool will never err, and models change over time, so ongoing monitoring is part of responsible use. For connecting the resulting assistant to your channels, see customer support chatbot or multilingual chatbot. For knowledge that also serves your website visitors, see FAQs and knowledge base articles.