Analyzing user feedback allows you to evaluate the relevance of Didask AI responses and identify areas for improvement in your knowledge base. This feature gives you an accurate picture of user satisfaction.
💡 Accessing the feedback interface
Feedback can be accessed in the Didask AI section > Feedback tab. This dedicated interface centralizes all the evaluations left by your users on the Didask AI's responses.
Feedback overview
Detailed evaluation table:
For each piece of feedback, you can view the:
Exact date and time of the interaction
AI response
User's reaction (thumbs up/down)
Tags (e.g., “Too wordy”) used to categorize issues
Optional user comments
And a Details button to access the full context of the conversation
Filtering and analysis
Filter by period: Analyze quality trends over time
Chronological view: Identify trends and peaks in user satisfaction/dissatisfaction
Analysis of negative feedback
Categories of identified issues
Users can qualify their negative feedback with specific tags:
Content issues:
Incorrect answer: Incorrect or outdated information in your knowledge base
Incomplete answer: Lack of information on the requested topic
Off-topic: The AI did not understand the question or drifted off topic
Format issues:
Too wordy: Responses are too long, lack of summary
Too brief: Responses are not detailed enough
Did not follow instructions: The AI did not follow the user's specific instruction
Detailed generation report
By clicking on “Details,” you can access the:
Full context of the conversation
Response generation time
Sources used by the AI
Entire conversation to understand the problem
✏️ Possible corrective actions
Improving the knowledge base
When faced with recurring negative feedback:
Add content on topics that are poorly covered
Update outdated information
Remove contradictory sources
Reorganize documents for better searchability
🤝 Escalation to the Didask team
Feedback is automatically aggregated by Didask
The technical team can identify and correct systemic issues
Contact support for recurring issues not resolved by content improvement
✨ Optimization tips
💡 Responsiveness: Review feedback weekly to quickly identify issues
💡 Communication: Inform your users of improvements made based on their feedback
💡 Qualitative analysis: Beyond volumes, read comments to understand expectations
💡 Post-correction follow-up: Verify that improvements effectively reduce negative feedback on the same topics
Keywords: AI Didask, coach, learning assistant, monitoring, quality of responses, feedback
Still have questions? Don’t hesitate to contact us at [email protected]. Our team is here to help and support you in all your projects! 💬