AI Chatbot for Your Website: From Knowledge Base to Launch

Setting up an AI chatbot means taking a chat assistant built on a large language model (LLM), feeding it your own content and connecting it to your website or messaging channels. The real work is not adding code. It is deciding what knowledge the bot answers from, within which limits, and when it hands the conversation over to a person. If you go in the right order, the first version launches with a narrow scope and then grows based on the questions people actually ask.

What is the difference between a rule-based chatbot and an AI chatbot?

A rule-based bot follows a predefined decision tree: the user taps a button and the bot returns the answer defined for that button. Its behavior is predictable, but it gets stuck when a question arrives that is not in the tree.

An AI chatbot understands a freely typed question and generates the answer on the spot. It is flexible, but without controls it can confidently make up an answer on a topic it knows nothing about. This is called hallucination, and much of the setup work is about managing that risk.

In practice, a hybrid is the healthiest structure for most businesses. Tasks that need an exact result, such as checking an order or booking an appointment, run through a rule-based flow, while open-ended questions like "is this product right for me?" are handled by AI.

What should you decide before setup?

1. The task definition. What will the bot do, and what will it not do? Write a scope such as "answers questions about products, delivery and returns; does not negotiate prices; gives no legal or medical opinions."
2. The success measure. Pick two or three indicators, such as the share of conversations the bot resolves on its own, the share handed over to a person and the number of leads coming through the bot.
3. The channel. Website first, WhatsApp or Instagram? Each channel has its own rules, and starting with one channel makes testing easier.
4. The handover rule. Write down from the start in which situations, at which hours and to whom a conversation is transferred.
5. Data responsibility. If personal data will come up in conversations, decide where the logs are stored and who can access them.

Should you use an off-the-shelf platform or a custom build via API?

Off-the-shelf chatbot platforms let you upload documents through a dashboard and go live by adding a code snippet to your site. They start fast and need no software team. Their limits are how far you can customize and how much control you have over where the data is processed.

A custom build using a model provider's API lets you connect the bot to your own systems: CRM, stock, orders or an appointment calendar. Answer rules, logging and permissions are in your hands. In return, it needs more development and regular maintenance.

A simple test helps you decide. If the bot will only share information that is the same for everyone (FAQs, return terms, opening hours), an off-the-shelf platform is often enough. If it needs to work with user-specific data (order status, account balance, available appointment slots), integration is unavoidable and a custom build starts to make sense.

How do you train a chatbot on your own knowledge base?

"Training" is a slightly misleading word here; in most business bots the model is not retrained. The method used is RAG (retrieval-augmented generation). Your documents are split into small chunks and loaded into a searchable index (a vector database). When a user asks a question, the relevant chunks are retrieved and passed to the model together with the question, and the model writes its answer based on that context. AWS's guide to RAG describes the process in these same steps and lists a guardrail layer that checks answers as a separate component of a production system.

The practical consequence is that the bot can never be better than your knowledge base. When preparing it, pay attention to the following:

• A single source of truth. Do not leave two different documents on the same topic in the index (an old and a new return policy, for example); the bot will repeat whichever one it finds.
• Question and answer format. Short FAQ entries written in the words customers actually use give more accurate results than a long PDF catalog.
• An owner for updates. Make it clear who updates frequently changing information such as prices, stock and campaigns, and how often.
• What stays out. Internal correspondence, contracts and documents containing personal data should not go into the index.

How do you stop the bot from giving wrong answers?

• A system instruction. The bot's role, tone and prohibitions should be written down. The most important line is usually: "If the answer is not in the knowledge base, say you do not know and refer the user to an agent."
• Answers with sources. The bot can link to the page an answer came from. Users can check it, and you can immediately find which document wrong information came from.
• A test set. Before launch, test the bot with real questions collected from call center logs and emails, and mark each answer as correct, incomplete or wrong. Rerun the same set after every major change.
• Sensitive topics. On price commitments, contract terms, health or legal matters, the bot can give information but does not make decisions; it hands the conversation to a person.
• Saying it is a bot. Users should know they are talking to software. Meta's Messenger and Instagram messaging policy requires the automated experience to be disclosed at the start of the conversation where the law requires it, and recommends it as good practice even where it is not legally required.

What about personal data and privacy?

Chatbot conversations carry personal data: names, phone numbers, order numbers and sometimes health or payment information. Check the data protection rules in each market you serve. For users in Turkey, the Turkish Personal Data Protection Authority's recommendations on AI call for minimal data use, continuous monitoring of model accuracy and building systems on data protection principles from the design stage onwards. The Authority has also published a separate guide that explains generative AI and personal data protection in 15 questions.

In practice, these four steps cover most of the work:

• Add a short link to your privacy notice in the chat window.
• Decide how long conversation logs are kept and who can access them.
• Check your provider's contract for where it processes data and whether it uses data to develop its models.
• Stop the bot from asking for information such as ID or card numbers in the chat; if needed, send the user to a secure form.

What should you measure after launch?

In the first weeks, the most valuable data is the conversation logs themselves. Once a week, read the questions where the bot said "I don't know," the conversations where it answered wrongly and the reasons conversations were handed over to a person. This list shows the gaps in your knowledge base, and it often reveals what is missing from your website's FAQ page too.

Indicators to track regularly: the share of conversations the bot resolves on its own, the handover rate, the ratings users give and the number of leads or orders coming through the bot. A mistake we see often is launching the bot and never looking at the logs; the bot then repeats the same error for months.

In short: the setup order for an AI chatbot

1. Write down the bot's task and limits.
2. Choose the first channel and the rule for handing over to a person.
3. Clean up the knowledge base and reduce it to a single source of truth.
4. Decide between an off-the-shelf platform and a custom build based on whether the bot needs user-specific data.
5. Test with a set of real questions.
6. Launch with a narrow scope and read the logs every week.

We explain how the bot connects technically to your website, WhatsApp and CRM in our separate article on chatbot integration. If you are planning an assistant that works with your own data, see our AI integration services, or get in touch to talk about your situation.