Building an AI Chatbot in Bubble.io: The Complete Technical Guide
A production-ready AI chatbot in Bubble.io is more than a text input connected to Claude. It needs conversation history management, a knowledge base, graceful error handling, and a UI that users actually enjoy interacting with. This guide covers all of it.
The Chatbot Architecture
A production Bubble.io AI chatbot has four layers: the UI layer (the chat interface the user interacts with), the data layer (the Bubble.io database storing conversations and the knowledge base), the AI layer (Claude API processing each message), and the knowledge retrieval layer (finding the right knowledge base content for each query).
The critical technical decision is conversation memory. Claude has no built-in memory between API calls — each call is independent. To maintain conversation context, you must pass the full conversation history in every API call. Bubble.io stores the conversation in a database; each new message triggers an API call that includes all previous messages. This produces natural, contextual conversation but increases the token count (and cost) as conversations grow longer. Set a maximum conversation history length (typically the last 10 to 20 messages) to control costs without losing contextual continuity.
Data Model Design
Conversation data type
Fields: unique_id (auto-generated), user (linked to User data type), started_at (date), last_message_at (date), status (text: active/archived), topic (text — AI-generated summary of the conversation topic, useful for displaying in a conversation list). This is the container for each conversation thread.
Message data type
Fields: conversation (linked to Conversation), role (text: user/assistant), content (text — the message text), created_at (date), token_count (number — optional, for cost monitoring), error (yes/no — flags messages where the AI call failed). Each message in the conversation is a separate record.
KnowledgeBase data type
Fields: title (text), content (long text — the knowledge content), category (text — for filtering), keywords (text — comma-separated, used for simple retrieval), last_updated (date), active (yes/no — to disable articles without deleting). This is the database of your business knowledge that Claude will reference when answering questions.
Build the knowledge retrieval logic
Simple retrieval (no external vector database required): when a user sends a message, extract the key terms (use a preliminary Claude call: from this message, extract 3-5 key search terms as a comma-separated list). Search the KnowledgeBase for records whose title or keywords contain these terms. Retrieve the top 3 to 5 matching records. Include their content in the Claude system prompt: ‘You are a customer service assistant for [company]. Answer questions using this knowledge: [paste retrieved content]. If the answer is not in the knowledge provided, say so clearly and offer to connect the user with a human team member.’
Building the Chat UI
Chat container structure
A Group element containing: (1) a Repeating Group displaying the conversation messages, scrolled to the bottom by default, (2) a text input for the user’s message, (3) a Send button, and (4) a loading indicator that appears while Claude is processing. The Repeating Group data source: Messages filtered by current_conversation, sorted by created_at ascending. Each message cell displays differently based on the role field: user messages aligned right with a navy background; assistant messages aligned left with an off-white background.
Sending a message workflow
Button click workflow: (1) Check that the text input is not empty. (2) Create a new Message record: role = user, content = input text, conversation = current_conversation, created_at = current date/time. (3) Clear the text input. (4) Show the loading indicator. (5) Retrieve the last 15 messages in this conversation from the database. (6) Build the messages array (using Bubble.io’s list operations to format each message as role/content pairs). (7) Retrieve the relevant knowledge base articles based on the user’s message keywords. (8) Call the Claude API with the conversation history and knowledge context. (9) Create a new Message record: role = assistant, content = Claude’s response. (10) Hide the loading indicator. (11) Scroll the Repeating Group to the bottom.
Streaming responses for better UX
Standard Claude API calls return the full response after processing is complete — for longer responses, this can mean 3 to 8 seconds of blank waiting. Streaming returns the response token by token as it is generated, like watching someone type. Bubble.io’s standard API Connector does not support streaming natively, but it can be implemented using Bubble.io’s backend workflows with Server-Sent Events or by using a middleware service (a Cloudflare Worker or a simple Express server) that streams the Claude response and updates a Bubble.io database record in real time. For most business chatbots: non-streaming is acceptable and significantly simpler to implement. Add streaming if your user testing reveals that the wait time is causing abandonment.
Knowledge Base Management Interface
Build a simple admin interface for maintaining the chatbot knowledge base: a page accessible only to admin users with a list of all KnowledgeBase records, a form for creating new articles, and an edit/delete capability. The AI can help maintain the knowledge base: when a support ticket is resolved with a new answer, an admin can click Add to Knowledge Base, which creates a draft KnowledgeBase record with the question and answer pre-populated. The admin reviews and approves. Over time, the knowledge base grows from the actual support interactions — the most reliable source of the questions users actually ask.
How do I prevent the chatbot from making up information?
The most effective hallucination prevention for a customer-service chatbot: constrain Claude to only answer from the provided knowledge base content. System prompt instruction: ‘Only answer questions using the knowledge content provided below. If a question cannot be answered from this knowledge, say: I don’t have that information in my knowledge base — let me connect you with a team member who can help. Never invent or guess information about [company name].’ This hard constraint, combined with a well-maintained knowledge base, produces a chatbot that is reliably accurate rather than confidently wrong.
How do I handle the handoff from AI to human agent?
Build the escalation workflow: when the chatbot response contains the phrase let me connect you with a team member (which you have trained it to use for out-of-scope questions), trigger a GoHighLevel or email notification to the support team with the conversation transcript. The customer receives a message: I've flagged this for a team member — you'll hear back within [time]. The team member opens the conversation in the admin view and continues in GoHighLevel or by email. The chatbot is clearly an AI and human handoff is always available — both are critical for trust.
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