AI Transparency Center
How Prakura AI Is Meant to Work
Plain-language explanations of how future AI features are intended to behave — and the controls you have today.
Principles in practice
Six things you should always know
Data Used
Only data a feature needs — for example course progress, coding results or a career goal you selected. In this website preview, everything is synthetic.
Why Recommended
Every recommendation shows the signals it was based on, so you can judge whether it fits.
Human Review
Recommendations about people — hiring, placement, performance — are reviewed by a person before anything happens.
Limitations
AI can be wrong or incomplete. Recommendations are a starting point, never a final decision or a guarantee.
Confidence
Where useful, results carry a High / Medium / Low confidence label in words — not colour alone.
User Control
You can pause suggestions, reset personalization and clear history at any time below.
Recommendation source UI
Every recommendation shows its sources
Example · Demo Recommendation
Take “API Testing: Postman to REST Assured” next
It closes the largest gap between your current skills and the SDET role.
Based on
- Course Progress
- Coding Performance
- Selected Career Goal
- Mock Interview Score
Confidence: Medium · Demo Recommendation
User-Controlled AI
Stored on this device only.
Hide personalised recommendations across the site.
Same as Reduce Motion in Accessibility Preferences.
Persona
Not set
Responsible AI
The principles behind it
Human oversight
People make consequential decisions; AI recommends.
Transparency
AI-generated content and suggestions are labelled.
Privacy
Minimum data, clear purpose, user control.
Explainability
Recommendations show the factors behind them.
Fairness
Outputs are reviewed for unintended bias.
Safety
Guardrails against harmful or off-topic output.
Evaluation
Features are tested against evaluation sets before release.
Accountability
Decisions are logged with who made them.