How to Add an AI Chatbot to Your Website the Right Way

Anyone can drop a generic chatbot on a site. The ones that actually help customers know your pricing, your policies and your product, and they say 'I do not know' instead of making things up. That difference comes down to retrieval.

Give the model your knowledge, not just a prompt

The pattern is called RAG (retrieval-augmented generation). You store your content (docs, FAQs, product data) as embeddings in a vector database, and at question time you fetch the most relevant pieces and hand them to the model as context. The model answers from your data, not from its training.

• Ingest your content and store embeddings (pgvector works great here).
• On each question, retrieve the top matching passages.
• Prompt the LLM to answer only from those passages, and to admit uncertainty.
• Log conversations so you can see what customers actually ask.

The result

A chatbot that reduces support load because it answers real questions accurately, with citations back to your own pages. That is a genuinely useful feature, not a gimmick, and it is very buildable today.