RAG Chatbot in Dify

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In this project, I created a small RAG chatbot in Dify. The purpose was to understand how a chatbot can use uploaded or imported content as its knowledge base instead of relying only on general model knowledge.

The chatbot was built so it could answer questions based on the information I gave it. I created a knowledge base for the chatbot and added relevant content that it could search in. This was important because the chatbot should not just make general answers, but answer from the specific material connected to the task.

Working with the knowledge base

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In Dify, I worked with adding content to the chatbot’s knowledge base. A knowledge base is the information source that the chatbot can search in before answering. This makes the chatbot more controlled, because it has a specific set of information to use.

I also worked with how the text is prepared for retrieval. When content is added to a RAG system, it is often split into smaller parts. These parts are easier for the system to search through when a user asks a question. The chatbot can then find the most relevant parts and use them as context in the answer.

This helped me understand that the quality of the knowledge base is very important. If the knowledge is clear and relevant, the chatbot has a much better chance of giving a useful answer.

Prompt and instructions

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Another important part of the project was writing the prompt for the chatbot. The prompt explained what the chatbot should answer, how it should behave, and what it should not answer.

I gave the chatbot instructions about staying within the knowledge base and not inventing information. This was important because a chatbot can otherwise try to answer even when it does not have enough information. I wanted the chatbot to only answer based on what it had been given.

The prompt also helped define the chatbot’s limits. For example, if the chatbot did not know the answer, it should not guess. It should either say that it does not have enough information or guide the user in a safer direction.

Testing the chatbot

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After setting up the chatbot, I tested it with different questions. The chatbot worked quite well. It answered based on the knowledge I had added, and it did not start answering about things outside the information it had been given.

This was a good result because it showed that the combination of a clear knowledge base and a clear prompt can make the chatbot more reliable. It also showed me that RAG is not only about uploading documents. The instructions around the chatbot are also important.

Reflection

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The project gave me a better understanding of how retrieval, indexing and generation work together in a RAG-based solution. Retrieval is about finding the relevant information, indexing is about making the content searchable, and generation is when the language model writes the final answer.

I learned that a RAG chatbot becomes more useful when it has both good data and clear rules. The knowledge base gives the chatbot information, while the prompt gives it direction and boundaries.

Overall, the chatbot worked as expected. It stayed close to the information I had added and followed the instructions I gave it. This made me see how RAG can be used to create a more controlled and practical chatbot solution.