Retrieval-Augmented Generation (RAG) allows your AI agent to use your specific data, but the quality of the output heavily depends on the quality and structure of that input data. Here's how to improve accuracy:
1. Provide Clear, Well-Structured Documents
Use clear headings, paragraphs, and lists in your source documents (PDFs, DOCX, etc.). Avoid large blocks of unstructured text. Well-organized content is easier for the system to index and retrieve relevant chunks from.
2. Be Specific and Avoid Ambiguity
Ensure the information in your knowledge base is precise. Ambiguous phrasing can lead the retrieval system to pull irrelevant context for the LLM.
3. Keep Information Updated
Regularly review and update your knowledge base documents or website content. Outdated information leads to incorrect answers.
4. Consider Chunking Strategy (If Available)
Some advanced RAG systems allow control over how documents are split into chunks for indexing. Smaller, focused chunks can sometimes improve retrieval relevance for specific queries. Check CreateMyChat.com documentation for any available options.
5. Use Diverse Data Formats (Where Appropriate)
Include FAQs, product descriptions, tutorials, and policy documents. A varied knowledge base allows the agent to answer a wider range of questions accurately.
6. Test and Iterate
Regularly test your agent with common and edge-case questions. Review conversation logs in the analytics to see where it struggles. Use these insights to refine your knowledge base content and structure.
By optimizing your input data, you empower your CreateMyChat.com agent to provide consistently accurate and helpful responses.