AI Supervision 9. AI Beyond the Web: Seamless Evaluation with SDKs and Mobile Integration
- TecAce Software
- 3 days ago
- 2 min read
"Our AI chatbot lives in a mobile app. Do we have to test it on a separate web dashboard?"
"Copy-pasting logs from our server to the evaluation tool is tedious."
Many AI evaluation tools are stuck in the browser sandbox. However, real users interact with AI in mobile apps, internal messengers like Slack, or complex backend workflows. The gap between the testing environment and the production environment often leads to unexpected bugs.
AI Supervision bridges this gap with robust SDKs and APIs, allowing you to embed evaluation capabilities wherever your code lives.
1. The Developer's Essential: Python SDK Integration
Python is the lingua franca of AI development. The AI Supervision SDK integrates into your existing codebase with a simple pip install.
Seamless Compatibility: Works effortlessly with popular frameworks like LangChain and LlamaIndex.
Automated Log Collection: With just one line of code, every prompt, response, and retrieved context is automatically sent to the AI Supervision server for tracking and scoring. Say goodbye to manual log transfers.
2. Expanding to Mobile Environments
Smartphones have different constraints—screen size, input methods, and network stability. To accurately measure the quality of an in-app AI chatbot, you need data from the device itself.
API Integration: Send conversation data generated within iOS or Android apps directly via API in real-time.
User Feedback Loop: Capture "Thumbs Up/Down" signals from app users and correlate real-world satisfaction with your technical evaluation metrics.
3. Automation in CI/CD Pipelines (Remote Evaluation)
True DevOps means testing runs automatically whenever code changes.
GitHub Actions Integration: Trigger the SDK to run your TestSet and report scores automatically whenever a developer pushes code.
Quality Gates: Implement logic like "Block deployment if Hallucination Rate > 10%." This systemically prevents unstable models from reaching production.
Conclusion: Evaluation Must Be Part of Development
Going to a separate website to run tests adds friction. Evaluation should be woven naturally into your development workflow.
Start seamless quality management in your pipelines, mobile apps, or servers today with the AI Supervision SDK.
Amazon Matketplace : AI Supervision Eval Studio
AI Supervision Eval Studio Documentation
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