Every response generated by Wonderchat’s AI is rooted directly in your connected knowledge sources — such as your website pages, FAQs, policy documents, or uploaded files. The system doesn’t “hallucinate” answers; it retrieves, summarizes, and cites verified content from your dataset before replying.

Here’s how it works step-by-step:

1. Retrieval from Trusted Sources
   When a user asks a question, Wonderchat’s AI runs a semantic search across your indexed content. It identifies the most relevant paragraphs, FAQs, or policy sections that best match the user’s intent.
   
   

2. Answer Grounding
   The AI then generates a response only using the retrieved data. It cross-verifies facts against multiple top results to ensure the final answer aligns with your approved content. This ensures accuracy, consistency, and factual grounding.
   
   

3. Link Transparency
   At the end of each response, Wonderchat automatically includes contextual links to the exact source materials — whether it’s a product page, help article, or policy document.

   • This lets users instantly navigate to the original content (“Read more” links).

   • It provides full transparency into how the answer was formed — showing exactly where the information came from.

   • It allows teams to verify or update their documentation easily when something changes.
     
     

4. Confidence Scoring
   Each response is accompanied by a confidence score, indicating how strongly the AI believes the answer aligns with verified knowledge. This helps teams evaluate chatbot reliability and continuously improve accuracy.
   
   

5. Feedback and Corrections Loop
   Admins can correct or refine an AI response directly from the dashboard. The system learns from these corrections, updating its internal retrieval and generation logic over time.