Chatbots are everywhere these days. Who hasn’t seen one pop up on a website, inside an app, or even built into your everyday work tools?

But here’s the thing: they’re not always perfect. Sometimes they’re too generic. Has it happened to you that when you ask a specific question about let’s say your business or your event, the answer feels a bit too vague so you end up feeling that the experience wasn’t very good? On the flip side, if you want a chatbot that’s actually tailored and truly helpful, building one from scratch can feel way too complicated.

RAG (Retrieval-Augmented Generation) it’s a technique that helps chatbots stay grounded in context, so they give answers that are accurate, specific, and supported by your own documents.

RAG chatbots can take your policies, event guides, manuals, or FAQs and turn them into clear, accurate answers with sources — all without needing to train a massive AI model. 

Now, you might be wondering, is this difficult to develop? It shouldn’t be! With Stack AI you don’t need to be an expert engineer to build one. Stack AI low-code workflows allow you to build a RAG Chatbot in MINUTES. In this guide, I’ll show you how to do it, step by step.

What is a RAG Chatbot?

A RAG (Retrieval-Augmented Generation) chatbot is an AI assistant that first retrieves relevant snippets from your own internal documents (like policies, PDFs, or guides) and then uses a language model to generate an answer based on that information.

Because it’s grounded in your content, the chatbot gives answers that are more accurate, easier to trust, and simple to update. This translates into clear answers instead of vague responses or made-up details.

Difference between a RAG Chatbot and a Traditional Chatbot

Not all chatbots are created equal. Traditional chatbots that don’t use RAG usually fall into two camps:

• Rule-based bots: these follow predefined flows or “if this, then that” rules. They can only respond to what’s explicitly programmed, which means, if the user ask a question outside of these rules it can get very frustrating for them!

• LLM-only bots: these use a language model to generate answers. What happens with these chatbots is that they’re limited to the knowledge available in the model’s training data, which stops at a certain date plus it doesn’t include your private documents.

RAG chatbots, on the other hand, anchor their answers in your documents. They pull in the freshest, most relevant information from your knowledge base before responding. That makes them far more trustworthy, adaptable, and practical in real-world settings.

🔗 Learn more: If you want to know more about the key benefits of RAG in 2025 or its main limitations, we recommend reading our dedicated articles.

What is the Architecture of a RAG Chatbot?

Behind the scenes, a RAG chatbot has a few moving parts — but don’t worry, you don’t need to be an expert dev to understand the big picture! Think of it like a conversation pipeline:

1. The user asks a question → (Technical term: Input node / query capture).

2. The chatbot goes to a document library to find the most relevant pieces → (Document ingestion + Pre-processing & chunking).

   1. Load files (PDFs, DOCX, HTML, CSV, URLs).

   2. Split them into smaller “chunks” so they’re easier to search.

3. It feeds those pieces into a language model → (Embeddings & index → Retriever → Re-ranker)

   1. Chunks are converted into vector embeddings and stored in an index.

   2. A retriever finds the top-k most relevant snippets for the question.

   3. A re-ranker improves the ordering of those snippets.

4. The language model then generates an answer and shows where the information came from → (Prompting/orchestration + LLM generation + Citations & guardrails)

   1. The system builds a prompt that combines the user’s question with the retrieved context.

   2. The LLM generates a grounded answer.

   3. Guardrails enforce rules (e.g., “don’t guess”) and citations are added for transparency.

5. Finally, that answer is displayed in a chat interface → (Evaluation, monitoring, and deployment)

   1. The bot’s responses can be evaluated for accuracy, faithfulness, and latency.

   2. Then the chatbot is deployed as a widget, API, or internal tool.

Now that we understand what a RAG chatbot is and how its architecture works behind the scenes, let’s bring it to life with a real-world example. Theory is useful, but seeing it in action makes it much easier to understand.

🔗 Learn more: If you want to know more about how to build RAG workflows with LLMs, we recommend reading our dedicated article.

Scenario: Festival Goer’s Guide ChatBot

Imagine you’re the organiser of “Echo Fields Festival 2025” and that you’ve run a few editions already. Every year your inbox and phone lines explode with the same questions: “Can I bring sealed water bottles?” “What’s the bag policy?” “Are day tickets re-entry?” “Where are the shuttle pick-ups?” Handling these one by one eats time, adds cost, and delays replies.

This year, you have come up with the great idea of launching a RAG chatbot on your website and app so attendees get instant, accurate answers sourced from your official documents.

Building our RAG ChatBot in StackAI

Let’s see how we can build this RAG ChatBot in StackAI:

Step 1 — Create the project

• In your Dashboard, click Create New Project.

• Select Workflow Builder (so we can build from a template).

• Click Create, and under Templates choose Knowledge Base Agent.

• Click use template and give it a name — e.g. Festival Goer’s Guide Chatbot — and click Create Project.

You’ll now see the default canvas with four blocks:

• Question → the user’s input.

• Documents → source knowledge.

• AI Model → the engine generating answers.

• Answer → the output shown to the user.

Step 2 — Upload the festival documents

Click on Documents. This is where you upload your knowledge base.

For our example we’ll use:

• Festival FAQs.

• Festival Event Guide.

• Festival Safety Policies.

Once uploaded, these become the foundation for the chatbot’s answers.

Step 3 — Configure the model

Click on the Model block.

• By default, Stack AI suggests Anthropic Claude 3.5. For this chatbot, that’s more power than we need.

• Instead, select OpenAI GPT-4.1, which is a great balance of accuracy, efficiency, and reliability for retrieval-based answers.

Now we will write a clear system prompt - basically an instruction to our AI model to guide its behaviour. We’ll customise it to keep the bot friendly, specific, and grounded:

You are the Festival Goer’s Guide Bot for Echo Fields 2025.

- Provide answers solely based on the official documents available.

- If the information is not found in the context, respond with: “I can’t find that in our current policies. Please contact support here: www.echofields.com.”

- Keep responses brief and friendly.

Step 4 — Connect to your Knowledge Base

In the model settings, select the Festival Goer’s Guide Chatbot knowledge base (the documents you just uploaded).

• Make sure Citations is turned on — this is crucial for transparency and user trust.

Step 5 — Adjust advanced settings

Open Advanced Settings and configure:

• Guardrails: turn on filters for toxic content, legal advice, and suicidal thoughts. This protects both your brand and your users.

• Temperature: set to 0 so answers are deterministic and consistent (every user gets the same response to the same question).

• Leave stream data and date/time on their defaults.

Step 6 — Test your chatbot

Save the project and try a few queries in the Question block.

Example:

• You: “Can I bring a refillable bottle to the festival?”

• Bot: “Yes, you can bring an empty refillable bottle. You can refill it at designated water stations. [Festival FAQs]

Notice the citation — it shows the answer came directly from the FAQ document.

Step 7 — Publish and export

Once you’re happy with testing:

• Click Publish.

• Go to Export and choose Website Chatbot (perfect for embedding on your festival site).

  • You could also select Chat Assistant for mobile app integration.

• Give it a name: Echo Fields Festival Assistant.

• Add a short description:

  “The Echo Fields Festival Assistant answers attendee questions grounded in official documents. It returns concise, trustworthy answers with citations, and falls back gracefully when info isn’t in the documents.”

• Set a placeholder like “How can I help you today?”

• Optionally upload a logo and tweak colors to match your brand.

  
  

  

At this point, you’ve got a fully working RAG chatbot that attendees can interact with on your website or app. 🎉

When we ask “Can I bring a power bank?” the chatbot provides an answer that is accurate, concise, and cites the FAQ document as the source. If we ask “Can I re-enter with a day ticket?” Again, the bot responds correctly and grounds the answer in our knowledge base.

Finally, we tested a question that isn’t covered in any of the uploaded documents: “Are drones allowed?” Exactly as we instructed in the system prompt, the bot handles the unknown and guides the user to support.

This shows the chatbot is behaving exactly as expected:

• It answers only from official documents.

• It provides citations to build trust.

• It handles gaps gracefully instead of guessing.

Wrapping Up

And that’s it — in just a few steps, you’ve gone from a pile of FAQs, event guides, and safety docs to a festival-ready RAG chatbot that can handle attendee questions 24/7.

And see? You didn’t need to write a single line of complex code. Stack AI’s low-code workflows make it simple to upload your content, configure retrieval, and deploy a chatbot your users can trust.

👉 Ready to try it yourself? Start building with StackAI today and see how fast you can turn your knowledge into a smart, reliable assistant.