Testing Generative AI: 3 Approaches to Ensure Quality, Accuracy and Safety

The rise of Generative AI (Gen AI) offers unprecedented capabilities in content creation, automation and personalization. However, the power and complexity of Gen AI necessitate rigorous testing to mitigate risks and maximize potential. For leaders in the Gen AI development space, understanding and implementing effective testing methodologies is not just a best practice — it’s imperative.

While automated testing plays a role, the dynamic nature of Gen AI demands a human-in-the-loop approach. Here are three key approaches, backed by real-world case studies, that showcase the importance of human insight in ensuring the quality, safety and efficacy of your Gen AI solutions:

1. Harnessing Human Feedback with Prompt Response Grading

Case Study: Financial Services Chatbot Refinement

A leading financial services firm sought to enhance its AI chatbot, built on an open-source large language model (LLM) and trained on proprietary customer data. To fine-tune the chatbot’s responses and align them with user needs, the company partnered with Applause to implement reinforcement learning from human feedback (RLHF).

A diverse team of testers evaluated thousands of chatbot responses on a weekly basis, grading accuracy and flagging harmful content such as bias, toxicity and inaccuracies. This enabled the company to identify specific weaknesses in the model, such as its inability to interpret idiomatic expressions. It then used this feedback to refine the chatbot, significantly reducing safety concerns and improving user satisfaction.

Key Takeaway: Integrating human feedback through prompt response grading is a powerful tool to uncover nuanced issues that automated tests may miss.

2. Proactive Risk Mitigation with Red Teaming

Case Study: Adversarial Testing for a Tech Giant’s Chatbot

A global technology leader sought to fortify its chatbot against adversarial prompts designed to elicit harmful responses. Recognizing the need for specialized expertise, the company partnered with Applause to assemble a red team of experts with deep knowledge in chemical and biological materials. This team generated extensive datasets, encompassing both offensive prompts and safe, appropriate responses to act as a point of comparison. These datasets were then used to train the chatbot to identify potentially dangerous usage patterns and respond responsibly.

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By proactively identifying vulnerabilities through red teaming, the company successfully implemented guardrails within the model, protecting users from harmful content and demonstrating a commitment to safety and security.

Key Takeaway: Red teaming is a proactive approach that goes beyond basic testing, simulating real-world attacks to reveal hidden biases and vulnerabilities.

3. Rigorous Pre-Launch Testing with a Trusted Tester Program

Case Study: Global High-Tech Company’s Chatbot Launch

In preparation for the global launch of its consumer chatbot, a global high-tech company partnered with Applause to ensure a successful launch. Applause quickly assembled a diverse team of 10,000 testers from six countries to participate in a comprehensive, four-week testing program. Testers engaged with the chatbot across a wide range of scenarios, generating thousands of prompts and providing qualitative feedback on the quality of responses.

The testing significantly improved the chatbot’s accuracy, performance and user satisfaction, resulting in an increase in the product’s Net Promoter Score (NPS).

Key Takeaway: Large-scale, pre-launch testing with diverse testers across different geographies and demographics ensures that your Gen AI solutions are thoroughly vetted for real-world scenarios.

Incorporate human expertise for real machine intelligence

The responsible development and deployment of Gen AI hinges on comprehensive testing. As the case studies above demonstrate, incorporating human expertise at every stage is critical for mitigating risks and ensuring user satisfaction. Whichever approach they take, leaders in Gen AI development must commit to rigorous testing to pave the way for a future where Gen AI truly benefits their users and business.