Contextual Assistance
Problem
Users often need guidance but may not know what to ask for or when to ask. Traditional help systems require users to interrupt their workflow to search for assistance.
Solution
Create intelligent assistance that proactively offers relevant help, suggestions, or information based on the user's current context, behavior patterns, and needs. The system should anticipate user requirements rather than waiting for explicit requests.
Examples in the Wild
GitHub Copilot
Suggests code completions as developers type, based on the current file context, project structure, and programming patterns.
Interactive Examples
Below are interactive examples that demonstrate contextual assistance in action. Try out these examples to see how AI can provide helpful suggestions based on your current context.
Text Editor with Smart Suggestions
This example demonstrates how contextual assistance can enhance writing by offering real-time suggestions.
Try it yourself:
- Type phrases like "I want to" to see suggestions
- Write longer text to see writing tips appear
Key Takeaway: Subtle suggestions can enhance productivity without disrupting workflow.
Search with Contextual Help
See how contextual assistance enhances search experiences with relevant tips based on what you're searching.
Try it yourself:
- Type "contextual" in the search box
- Clear the search to see recently searched terms
Key Takeaway: Context-aware search tips help users craft better queries quickly.
Learning Points
- Contextual assistance should be subtle and non-intrusive, appearing only when it's likely to be helpful.
- Always provide clear ways for users to accept, modify, or dismiss AI-generated suggestions.
- Contextual suggestions should be relevant to the user's current task or context to be truly helpful.
Implementation & Considerations
Implementation Guidelines
Make assistance subtle and non-intrusive; don't interrupt the user's flow
Provide clear indications that suggestions are AI-generated
Allow users to easily accept, modify, or dismiss suggestions
Gradually improve suggestions based on user feedback and acceptance patterns
Offer ways to access more detailed help when contextual assistance isn't sufficient
Design Considerations
Balance between proactive help and avoiding unnecessary interruptions
Consider privacy implications of analyzing user behavior to provide contextual help
Ensure the system doesn't make assumptions that could frustrate users if incorrect
Provide transparency about why certain suggestions are being made
Include settings to adjust the frequency and type of assistance