Step 1: Set Up Your Dify Instance

Visit dify.ai to use the cloud version, or deploy the open-source version locally using Docker. For this tutorial, the cloud version is simplest. Create an account and navigate to the dashboard. You will see options to create new applications — choose “Chatbot” as the application type.

Step 2: Create a Knowledge Base

Click on “Knowledge” in the sidebar and then “Create Knowledge.” Give it a name like “Product Documentation.” Upload your documents — Dify supports PDF, Word, TXT, Markdown, and HTML files. You can also sync content from websites by providing a URL. For best results, upload clean, well-structured documentation rather than raw, unformatted text.

Step 3: Configure the RAG Pipeline

After uploading, Dify will automatically chunk your documents and create embeddings. You can customize the chunking strategy: choose between automatic segmentation, custom delimiters, or paragraph-based chunking. Set the chunk size to 500-1000 tokens for most documentation. Select an embedding model — OpenAI’s text-embedding-3-small is a good default. Click “Save and Process” to start indexing.

Step 4: Design the Chatbot Workflow

Return to your chatbot application and open the visual workflow editor. Add a “Knowledge Retrieval” node that queries your knowledge base. Connect it to an LLM node that uses the retrieved context to generate an answer. Configure the LLM prompt to instruct the model to answer based only on the provided context and to say “I don’t have that information” when the context doesn’t contain the answer.

Step 5: Configure the LLM Provider

In the LLM node settings, select your preferred model provider and model. GPT-4o-mini is a cost-effective choice for customer support chatbots. Set the temperature to 0.3 for consistent, factual responses. Configure the system prompt to define the chatbot’s personality and behavior guidelines.

Step 6: Test Your Chatbot

Use the built-in preview panel to test your chatbot. Ask questions that should be answerable from your documentation and verify the responses are accurate and properly sourced. Ask questions outside your documentation scope to verify the chatbot correctly declines to answer. Check the “Citations” section to see which document chunks were retrieved for each answer.

Step 7: Deploy and Integrate

Once testing is complete, click “Publish” to deploy your chatbot. Dify provides a web widget you can embed on your website, a shareable chat URL, and a REST API for custom integrations. Configure the API key and rate limits in the settings, then integrate the chat endpoint into your existing support system or website.

By following these steps, you can deploy a production-ready, RAG-powered chatbot that provides accurate answers from your own documentation in under an hour — a task that would traditionally take days or weeks of custom development.