What kind of applications do you think companies are building with the power of AI and how do you think you can test them?

1. Chatbots & Virtual Assistants
2. Recommendation Systems
3. Image & Video Recognition Tools

Companies today are building a wide range of AI-powered applications across industries.

Types of AI Applications Companies Are Building

1. Customer Experience & Support

• Chatbots and virtual assistants (like me!)
• Sentiment analysis tools
• Personalized recommendation engines

2. Healthcare

• AI for medical imaging diagnostics
• Predictive analytics for patient outcomes
• Virtual health assistants

3. Finance

• Fraud detection systems
• Algorithmic trading platforms
• Credit scoring models

4. Retail & E-commerce

• Inventory optimization
• Dynamic pricing engines
• Visual search and product tagging

5. Manufacturing & IoT

• Predictive maintenance
• Quality inspection using computer vision
• Supply chain optimization

6. Autonomous Systems

• Self-driving vehicles
• Drones and robotics
• Smart home automation

 How You Can Test AI Applications

Testing AI systems is different from traditional software testing. Here's how you can approach it:

✅ 1. Data Validation

• Ensure training data is clean, unbiased, and representative.
• Validate preprocessing steps and feature engineering.

✅ 2. Model Testing

• Accuracy, precision, recall, F1-score for classification models.
• RMSE, MAE for regression models.
• Use cross-validation to check model generalizability.

✅ 3. Functional Testing

• Verify that the AI behaves as expected in different scenarios.
• Test edge cases and adversarial inputs.

✅ 4. Performance Testing

• Measure inference time, scalability, and resource usage.
• Stress test under high load conditions.

✅ 5. Explainability & Fairness

• Use tools like SHAP or LIME to interpret model decisions.
• Check for bias across different user groups.

✅ 6. Integration Testing

• Ensure the AI component integrates well with other systems (APIs, databases, UI).
• Validate end-to-end workflows.

✅ 7. Continuous Monitoring

• Monitor model drift and performance degradation in production.
• Set up alerts for anomalies or unexpected behavior.

• Companies are building AI-driven apps like:

  • Chatbots that understand real conversations

  • Fraud detection systems that spot unusual patterns

  • Recommendation engines that feel almost psychic

  • Predictive tools that help in decision-making

• To test these, I would focus on:

  • Checking how the AI handles real-world edge cases

  • Validating the accuracy and fairness of the model

  • Monitoring performance as data changes over time (data drift)

  • Making sure the AI stays reliable, ethical, and transparent

Because with AI, it’s not just about working—it’s about working right.

I work for a company that specializes in developing and testing insurance applications. We leverage an AI-based application to generate scenarios and streamline our process by using AI to create test cases, significantly reducing time and effort.

Writing Test Cases

Writing Automation code

Executing the test case using AI agents

Fixing Bugs 

Companies are making mostly RAG based application and AI agents which are aimed to serve role in solving solution of support chat , or getting queries answered from 100s of data documents.

How we can Test it ?
1.We can test it with different testing techniques like below:

Temperature Testing
Zero shot testing
contextmanagement testing
style transfer testing

2.Another way to test it it using LLM as Judge techniques which will draw and five us metrics like :
Contextwithoutreference
contextrecall
Faithfulness
Response relevancy
Factual Correctness
Accuracy metric

I do not want to put an AI generated answer here, rather would refer typing it entirely.
So, its a simple answer: AI is infused everywhere.
And we internally use AI to test AI, something like an LLM-as-a-Judge with combination of AI agents.

Companies use AI to build apps like chatbots, recommenders, and tools that predict trends or create content.

To test them:

• A/B testing for real-world performance

• User testing to assess usability and trust

• Monitor performance over time

Chatbots 

NLp

Computer vision 

Recommendation system 

Analysis 

SAP GUI AI Agents: Test mimic functional user role, day to day UAT scenarios..

Requirement → Test Cases → Automation: Test using assertions and Reusability for Automation, LLM as Judge along with human for Manual Test Cases,,,

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