How To Create An Accurate AI Knowledge Base

How To Create An Accurate AI Knowledge Base

The most integral part of building advanced AI agents is your knowledge base. Whether you're building AI voice agents, text-based chatbots, [or AI tools](https://www.topview.ai/blog/detail/find-your-perfect-ai-tools-aitools-artificialintelligence "Find Your Perfect AI Tools! #aitools #artificialintelligence"), a poorly created knowledge base will result in inaccurate answers and confused users. Think of it like a stack of playing cards — the knowledge base is the bottom layer, and without it, everything crashes down.

In this article, I will share some secrets that I use to improve my clients' AI knowledge-based responses. This will include structuring your documents, improving queries for better results, and uploading different document types, among other things. I will demonstrate this using Voiceflow and AI Chatbot Builder, but the method will work for any knowledge-based platform like vy.

Structuring Documents

Jumping into Voiceflow, one simple trick can be applied to any knowledge base. If you're not familiar with Voiceflow, it is a no-code chatbot builder. One of its powerful features is its knowledge section, where you can upload numerous files. Unfortunately, Voiceflow’s knowledge base has limited functionality, but you can still get more accurate results by:

1. Using More Chunks: Default chunk setting in Voiceflow is three, but it can be increased for more accuracy at a higher cost.
2. Better Document Structure: Ensure related information is grouped together to avoid mixed results. This can significantly improve the relevance of the responses.

Advanced Customization with Vector Shift

To take this step further, I'll be using Vector Shift. Vector Shift allows for a fully customizable knowledge base. Here are some ways Vector Shift can improve our responses:

1. Changing Chunk Size and Overlap:

   • Chunk Size: Larger chunks bring more relevant information at a higher cost.
   • Chunk Overlap: Provides context by having chunks overlap, creating more accurate responses.

2. Uploading Different Document Types:

   • Support for structured data like Excel sheets or CSV files.
   • Advanced querying for graphs, images, charts, and scanned PDF files using OCR.

3. Hybrid Keyword Search:

   • Combining traditional keyword search with chunk analysis for added accuracy.

4. Three Different Querying Options:

   • Transform Query: Converts poorly written queries into more effective questions.
   • Expand Query: Breaks down complex queries into simpler sub-questions for better response structuring.
   • Answer Multiple Questions: Handles multiple queries by answering one question at a time.

Implementing Vector Shift

With Vector Shift, it’s easy to create a more robust knowledge base:

1. Create [a Knowledge Base](https://www.topview.ai/blog/detail/what-is-knowledgebase-one-knowledge-base-all-the-answers "what is knowledgebase one knowledge base all the answers"):

   • Adjust chunk size and overlap settings.
   • Enable advanced querying options like Llama Pass for OCR.

2. Deploy Using API:

   • Customizable integration with any platform using API calls.

3. Hybrid and Query Transformation:

   • Enable hybrid keyword search for a combined relevance approach.
   • Transform and expand queries to handle poorly structured questions and multi-step inquiries effectively.

By following these strategies, you’re set to significantly improve the accuracy of your AI knowledge base, making your AI agents more intelligent and reliable.

Keywords

• [AI Knowledge Base](https://www.topview.ai/blog/detail/ai-powered-knowledge-management "AI-Powered Knowledge Management")
• Voiceflow
• Vector Shift
• Chunk Size
• Chunk Overlap
• Advanced Querying
• Hybrid Keyword Search
• Transform Query
• Expand Query
• Answer Multiple Questions
• OCR

FAQ

1. What is the importance of a knowledge base in building AI agents? A knowledge base is crucial as it forms the foundation of AI agent responses. Without a well-structured knowledge base, AI agents would provide inaccurate answers and confuse users.

2. How can I improve the accuracy of my AI responses in Voiceflow? Increase the number of chunks used, and ensure your documents are well-structured so related information is grouped together.

3. What is Vector Shift and how does it improve AI knowledge bases? Vector Shift is a platform for creating customizable knowledge bases. It allows modifications in chunk size and overlap, advanced querying, and supports multiple document types, improving the AI’s response accuracy.

4. How does adjusting chunk size and overlap affect AI responses? Larger chunks include more relevant information and overlapping chunks provide context, which can make responses more accurate though at a higher computational cost.

5. Can Vector Shift handle complex document types? Yes, Vector Shift supports structured data like CSV files and can query graphs, images, and scanned PDFs using OCR.

6. How can hybrid keyword search help in AI responses? By combining traditional keyword search with chunk analysis, the AI can pull more relevant data for responses.

7. What are the three querying options in Vector Shift?

• Transform Query: Converts poorly phrased questions into better ones.
• Expand Query: Divides complex questions into simpler ones for comprehensive answers.
• Answer Multiple Questions: Breaks down multiple inquiries and answers them one by one for clarity.