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AI chatbots work by combining three steps: the business provides content, the system indexes it so it can search it, and when a visitor asks a question, the system finds relevant passages and uses a language model to turn them into an answer. Most website chatbots use a technique called retrieval-augmented generation (RAG), which means the answer is based on the business's own content rather than the model's general knowledge. This keeps answers accurate and specific to the business.
Training data. This is the content the chatbot learns from: website URLs, help articles, FAQs, PDFs, or other text. The business provides this content; the system does not invent information from elsewhere. Answer quality depends on how accurate and complete the training data is.
Indexing. The content is split into chunks (small segments of text), converted into numeric representations (embeddings), and stored in a searchable index. Indexing lets the system quickly find which passages are most relevant to a given question. Chunk size and how chunks overlap affect how well the system can retrieve full context.
Retrieval. When a visitor asks a question, the system converts the question into the same numeric format and searches the index for the chunks that best match it. This is usually done with vector similarity search: the closer two vectors are, the more related the content. The top-ranked chunks become the context for the answer.
Answer generation. A large language model receives the retrieved chunks and generates a natural-language answer. Well-designed systems instruct the model to base the answer only on the provided chunks and to say "I don't know" when the content does not contain enough information. This reduces hallucinations—answers that sound plausible but are wrong or off-topic.
The visitor types a question. For example: "What is your return policy?"
The system converts the question into a search query. The question is turned into a numeric embedding so it can be compared with the indexed content.
The system searches the index. It finds the chunks that best match the question, typically the top 3–5 passages.
The system sends the chunks to the language model. The model is instructed to answer using only this content and to avoid making things up.
The model generates an answer. It synthesizes the retrieved passages into a coherent response in natural language.
The visitor receives the answer. The response appears in the chat interface in real time.
The entire flow usually takes a few seconds. Platforms like SiteBotGPT follow this pattern: add sources, train the bot, and embed it on the site.
Website-trained chatbots (also called RAG-based or knowledge-grounded chatbots) are trained on content the business provides. They answer from that content only. When the content does not contain the answer, they can say "I don't know" or offer handoff. This makes them more accurate and on-brand for business support, because they cannot invent policies or product details.
Generic chatbots often rely on the language model's pretrained knowledge. They can answer a wide range of questions but may give generic, outdated, or incorrect information that does not reflect the business. They are less suitable for customer support where accuracy and brand consistency matter.
Accuracy and scope. Website-trained chatbots have a narrow scope—they only know what they were trained on—but within that scope they tend to be more reliable. Generic chatbots have broad scope but lower reliability for business-specific questions.
What they can't do. AI chatbots typically cannot: check real-time inventory, perform account actions (e.g. change a password, process a refund), access external systems without custom integration, or answer questions about events or data that occurred after the last training.
When they fail. They may fail when: the question is vague or ambiguous, the answer is spread across many chunks and the retrieval misses some, the training content is outdated or wrong, the visitor asks about something outside the training data, or the question uses different wording than the content (though embedding search usually handles phrasing variations).
Hallucinations. Even with RAG, models can sometimes add or alter information. Constraining answers to the retrieved content and instructing the model to decline when unsure reduces this risk but does not eliminate it entirely.
1. What is RAG?
RAG stands for retrieval-augmented generation. The system retrieves relevant passages from the business's content and uses them to augment (support) the language model when generating an answer. The model does not rely on its pretrained knowledge alone.
2. How long does indexing take?
It depends on content volume. A few pages may take under a minute; larger sites or many documents can take several minutes to an hour. The business usually triggers indexing (often called "training") manually after adding or updating sources.
3. Can the chatbot learn from new content automatically?
Typically no. Most platforms require the business to add new sources and run training again. Some offer scheduled sync or retrain when content changes, but this varies by platform.
4. Why does the chatbot sometimes say "I don't know"?
This happens when the retrieved content does not contain enough information to answer the question confidently. It is a safety feature: the bot declines rather than guessing. Many systems are configured to offer handoff to a human in this case.
5. Do AI chatbots work in multiple languages?
It depends on the platform and model. Many models handle multiple languages; some platforms require separate content or configuration per language. Businesses serving multilingual customers should verify the platform's language support.
Understanding how AI customer support chatbots work helps businesses set realistic expectations. The bot is only as good as the content it is trained on—gaps in the knowledge base lead to gaps in answers. Knowing the flow (training data → indexing → retrieval → generation) makes it clear that maintaining and updating content is essential. It also clarifies why website-trained chatbots are better suited for support than generic ones: they ground answers in the business's own materials. Finally, understanding limitations helps businesses decide when to rely on the bot and when to escalate to humans.
Last updated: February 2025