How to Create a Step-by-Step: Advanced chatbot workflow in n8n example

Discover how to construct an intelligent, knowledge-driven assistant that answers questions with context and clarity.

The journey to a truly helpful company chatbot begins not with more data, but with smarter connections. We've already laid the groundwork with two powerful workflows: one to ingest and process documents into a searchable knowledge base, and another to act as a dedicated retrieval engine. Now, it's time to bring it all to life by building the conversational brain that will leverage this system.

This final piece, the advanced chatbot workflow, is where the magic happens. It’s the friendly front-end that users interact with, powered by the robust intelligence we built behind the scenes. Let's walk through the construction process. You can follow along with The Transcendent's platform or any similar workflow automation tool.

Getting Started: Setting Up Your Chatbot’s Foundation

Step 1: Create the Chatbot Workflow and Add a Chat Trigger

The very first step is to create a new, blank workflow. Think of this as the empty canvas for your intelligent assistant. Once you have your new workflow, you'll need to add a Chat Trigger node. This node is the digital doorway—the entry point—where all user questions and interactions will enter your chatbot system. It makes the conversation available to any chat user interface you might connect, whether it's a custom web app or an embedded chat widget. For now, you can skip configuring any extra fields; the primary goal is to establish this initial connection point. Ensure this node is at the very beginning of your workflow, ready to receive incoming messages.

Step 2: Connect an AI Agent Node and Define its System Prompt

Next, you'll connect an AI Agent node directly to your Chat Trigger. This agent node is the brain of your chatbot. The most critical ingredient for this agent is its system prompt—its instruction manual. This isn't just a simple "be helpful" directive; it's a sophisticated set of instructions that will guide the agent's behavior and define how it interacts with users and tools. Our prompt has three sophisticated jobs:

Tool Calling: It explicitly instructs the agent to call a specific tool, which we will name "GET HR policy" (or a similar descriptive name if your knowledge base is different). This means the agent fetches information by actively calling this tool, rather than relying on any information it might have internally or what's directly in the prompt. This offloads the knowledge retrieval to a specialized system.
Query Refinement: It teaches the agent to handle broad, open-ended questions by asking follow-up questions to narrow the scope. This solves a common problem in RAG (Retrieval-Augmented Generation) architectures where asking for "all benefits" might retrieve incomplete data due to limitations in the number of text chunks retrieved. By prompting the user to specify their query (e.g., "Are you looking for health benefits or retirement plans?"), the chatbot can retrieve more accurate and relevant information.
Citations: It mandates the use of citations. Every answer generated from the knowledge base must include a footnote citing the source document. This builds trust and allows users to verify information. The prompt will include an example format, such as [1] Source Document Name - [Link]. This ensures transparency and helps users explore the original source if needed.

You'll copy this detailed system prompt and paste it into the system message field within your AI Agent node. This robust prompt ensures your agent acts as a skilled librarian, not a know-it-all professor.

Visualizing the System Prompt Configuration

Imagine a clear, structured text box within the AI Agent node's configuration. Inside, you would see lines of text, almost like a script. The first few lines clearly state, "You are an HR assistant. Always use the 'GET HR policy' tool for information." Following this, there's a conditional instruction: "If a user asks a broad question (e.g., 'What are all the benefits?'), ask clarifying questions first to narrow the scope." Finally, a precise format for citations: "When providing information from the knowledge base, always include a footnote like this: [1] Document Title (Link)." This detailed textual prompt is the brain's instruction manual.

Step 3: Pick a Chat Model and Lock Sampling Temperature to Zero

With the rules of engagement set, we now select a chat model. A key advantage of this architecture is the decoupling of the conversational model from the embedding model. This means you can choose any model you prefer for the conversation; the retrieval logic is outsourced to our separate workflow, which simply accepts a search query and returns relevant text chunks. For this build, a powerful model like Gemini 2.5 Pro is an excellent choice for its reasoning capabilities. It's available through Google AI for Developers and Google Cloud, with 2.5 Flash variants offering speed for latency-sensitive applications. Access the model's options and adjust the sampling temperature to zero. This is crucial for consistent, factual responses, as a higher temperature can introduce creativity and potential inaccuracies, which are undesirable for a factual HR assistant.

Step 4: Add Lightweight Conversation Memory

To make conversations coherent, we add a memory node to the agent. Select a basic memory type, such as a window buffer, and set the context window to remember the last 20 messages of a dialogue. This allows the chatbot to maintain context across recent interactions, making the conversation feel more natural and less disjointed. If you anticipate long breaks between user interactions or session changes, you might need to plan for session IDs or persistence mechanisms to ensure the conversation history doesn't vanish. For most immediate HR queries, a modest context window is sufficient.

Step 5: Wire Up the Tool That Actually Retrieves Knowledge

Finally, we reach the most important part: tool use. We configure the AI agent to "Call an n8n workflow tool" (or a similar option if using a different platform). You'll name this tool explicitly: "GET HR policy" (or your chosen name) with a clear description: "This tool will search the HR knowledge base." This description is vital as it informs the chatbot when and why to use this specific tool for HR information. For better organization, you might label the tool node similarly within your workflow.

You will then select your pre-built "Chatbot Data Retrieval" workflow (or whatever you named your retrieval workflow) from the list. The input parameters for that workflow will automatically appear. Click the AI icon next to its "query" input parameter. This allows the chat model to automatically fill it with a user's question, which will be transformed into an effective search query for your knowledge base. This completes the essential configuration for an AI agent that draws from your internal database.

Illustrating Tool Configuration

Picture a setup screen where you create a new "tool." You type "GET HR policy" as its name and "Searches the HR knowledge base for company policies" as its description. Then, a dropdown menu allows you to select your "Chatbot Data Retrieval" workflow. Below that, an input field labeled "query" appears, and next to it, a small AI icon. Clicking this icon visually links the chatbot's understanding of the user's question directly to this tool's input, making the whole process dynamic and intelligent. This is where the agent connects its natural language understanding to the actual data retrieval mechanism.

Testing Your Smart Assistant: A Real-World Test Drive

Step 6: Test the Behavior and Verify Tool Calls

The setup is complete. Time for a test drive! Open the chat interface for your chatbot:

Simple Greeting: Start with a simple "hi." The agent should respond with a friendly greeting without invoking any tools. For example, "Hello, I'm your HR helper. How can I assist with HR rules today?" This confirms it can handle basic conversational turns using its internal language model without needing to search the knowledge base.
Specific Query with Tool Use: Now, pose a specific query that requires information from your HR documents, such as "Where do I apply for vacation?" Instantly, the agent should activate the "GET HR policy" tool. In the traces or logs of your workflow, you'll see the process: The tool received a search term like "vacation application" (generated by the chat model from your question). The retrieval workflow then finds the relevant text chunks from the HR policy documents. The chat model synthesizes this into a clear, step-by-step answer, complete with a source citation.
Semantic Search Test (Absent Topic): The system's true power is revealed with a semantic search query for a topic that isn't explicitly mentioned in your documents: "How can I get a MacBook?" The tool will query "how to get a MacBook." Since the word "MacBook" isn't in any document, the system retrieves information about semantically similar concepts, such as "personal laptops" or "company equipment" policies. The chatbot then provides a nuanced answer based on these related policies, demonstrating it understands meaning beyond mere keywords. For instance: "Per IT guidelines, company supplies computers. For specials, consult IT; personal devices typically barred for work." (Your specific response may vary based on your documents). This confirms its ability to handle queries beyond exact keyword matches, leading to richer outcomes.

With these tests, you verify that your chatbot is functional, uses tools appropriately, and leverages semantic understanding for comprehensive answers.

Step 7: Ship with Citations On by Default

To maintain transparency and trust, always keep the footnote format in the system instructions so references are consistently included. If your users require different citation styles, simply adjust the prompt to match their specific needs. This ensures that every answer provided by your intelligent assistant is backed by verifiable sources, reinforcing its reliability and usefulness.

What’s New and Notable: Advanced and Upgraded Options

The landscape of AI automation is constantly evolving, and recent advancements offer exciting upgrades for your chatbot:

Gemini 2.5 Pro (Generally Available): Google's flagship Gemini 2.5 Pro model now offers "adaptive thinking" and significantly enhanced reasoning and coding capabilities. It's available through Google AI for Developers and Google Cloud. For speed-sensitive use cases, the 2.5 Flash variants are excellent lightweight counterparts. Consider Pro for complex queries requiring deep understanding and Flash for high-throughput agents where quick responses are paramount.
Chat Trigger Enhancements: The Chat Trigger node now comes with official documentation and improved ecosystem support. This allows for seamless connection to your own custom front-end applications or the official chat packages, making it easier to embed your assistant with proper CORS (Cross-Origin Resource Sharing) and authentication mechanisms.
Formalized Workflow Tool Calling: The "Call n8n Workflow Tool" (or its equivalent in other platforms) formalizes the pattern of agents invoking separate workflows as tools. This makes the "agent → tool → workflow → output → agent" loop a first-class design paradigm, streamlining the creation of complex, modular automations.
Pinecone Vector Store Integration: If your chosen platform supports it, direct integration with vector databases like Pinecone is now more robust. The Pinecone Vector Store node can integrate directly with AI agents and retrievers, allowing you to plug RAG capabilities into your tools connector or utilize out-of-the-box QA/retriever patterns for highly efficient knowledge lookup.
Image-Focused Upgrades: For assistants that require visual interaction, image generation, or editing capabilities, models like Gemini 2.5 Flash Image are rolling out to creative tools (e.g., Firefly/Express). This is particularly handy for mixed-modal helpdesks (where users might upload screenshots) or marketing assistants that need to generate visual content.

Conclusion: Building Intelligent, Trustworthy AI Assistants

We've walked through crafting an advanced chatbot, from prompt setup to tool integration, highlighting how semantic search elevates user interactions. By separating the roles of data ingestion, retrieval, and conversation, we create a system that is powerful, scalable, and deeply integrated with our knowledge. The result is an assistant that doesn’t just chat—it understands, reasons, and provides sourced answers, transforming how a company supports its employees.

With a Chat Trigger for the front door, an AI Agent that knows when to call its specialized "GET HR policy" tool, a robust retrieval workflow behind the scenes, and footnote citations for trust, this setup empowers teams with a reliable HR assistant. It handles broad questions gracefully and answers with verifiable evidence.

Key takeaway: A well-designed AI agent acts as a skilled librarian, not a know-it-all professor. It doesn't need all the answers in its head; it just needs a perfect map to the warehouse where those answers are stored and the wisdom to know how to retrieve them. Remember: a smart workflow is the ultimate corporate therapist—it always knows where to look things up. Build the bot like a helpful librarian—ask for specifics, fetch from the stacks, and stamp every answer with a citation. Professional humor bonus: treat sampling temperature like hot sauce—start at zero and only add heat if everyone at the table agrees!

Table Summary: Advanced Chatbot Workflow Steps

Step	Headline	Description
1	Create the Chatbot Workflow and Add a Chat Trigger	Establish a new workflow and insert a Chat Trigger node as the conversation entry point.
2	Connect an AI Agent Node and Define its System Prompt	Link an AI Agent, instructing it to use tools, refine broad queries, and include citations for answers.
3	Pick a Chat Model and Lock Sampling Temperature to Zero	Select a strong reasoning model (e.g., Gemini 2.5 Pro) and set temperature to 0 for consistent, factual responses.
4	Add Lightweight Conversation Memory	Attach a memory node (e.g., window buffer of ~20 turns) to maintain conversation context.
5	Wire Up the Tool That Actually Retrieves Knowledge	Configure "Call n8n Workflow Tool" to point to your retrieval workflow, naming it clearly and setting its query input dynamically.
6	Test the Behavior and Verify Tool Calls	Test greetings, specific queries with tool use, and semantic search for absent keywords to confirm functionality.
7	Ship with Citations On by Default	Ensure the system prompt includes citation formatting for all knowledge-based responses to build trust.

Frequently Asked Questions

Q: Do we have to use the same model for embeddings and chat?

A: No. That's the beauty of this architecture. The model that creates the vector embeddings for search is completely separate from the model that powers the chatbot conversation. You can mix and match based on your needs and budget. For example, you might use a highly optimized embedding model for indexing and a powerful reasoning model like Gemini 2.5 Pro for the chat interface.

Q: Why force the agent to narrow broad questions?

A: The retrieval tool typically returns only a limited number of semantically closest text chunks. If a user asks a very broad question like "tell me everything about benefits," the retrieved chunks might be incomplete, leading to an inaccurate or partial answer. By forcing the agent to ask clarifying questions, it can narrow the scope, ensuring more focused and accurate replies based on the available, relevant information.

Q: How do citations work here?

A: Citations are enabled by including specific instructions in the system prompt. This prompt tells the agent to append a footnote-style reference (e.g., "[1] Document Name - Link") to any answer generated from the retrieved knowledge. The agent pulls the document name and URL from the metadata of the retrieved text chunks provided by the retrieval workflow.

Q: What memory length should we use for the chatbot?

A: Starting with a small context window, such as 20 turns (messages), is often a good practice. Longer windows risk conversation drift and can incur higher processing costs. If your conversations are expected to span long gaps or across different sessions, you would need to implement session IDs or persistent memory mechanisms to prevent the history from vanishing.

Q: Can this chatbot plug into a website or app?

A: Absolutely! The Chat Trigger node is designed for this. You can integrate it with your custom front-end application using APIs or utilize official chat packages and libraries provided by your workflow automation platform. You would also need to configure proper CORS (Cross-Origin Resource Sharing) settings and authentication to ensure secure embedding.

Q: What if the knowledge base lacks an exact keyword (e.g., “MacBook”)?

A: This is where semantic search truly shines. If a user asks about "MacBook" and that exact keyword isn't in your documents, the system will use its understanding of meaning to return conceptually close chunks (e.g., "personal laptop policy," "company equipment guidelines"). The AI agent can then use this semantically related information to formulate a careful and relevant response, complete with citations, even without an exact keyword match.

SEO & Non-Essential Information

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Meta Description:

Learn to build an advanced AI chatbot using RAG architecture and workflow automation. Our step-by-step guide covers system prompts, tool integration, Gemini 2.5 Pro, and semantic search for intelligent, cited responses.

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