The rapid rise and adoption of generative artificial intelligence (GenAI) has had transformative effects on automation, personalization and content creation. But as companies look to maximize its potential while managing risks, they must prioritize rigorous testing. Automated testing is part of the process, but the dynamic and complex nature of GenAI makes testing with real users more essential to ensure accuracy, quality and safety.
Here are three approaches to consider:
1. Prompt Response Grading
Human feedback can and does uncover issues often overlooked by automated testing. When building an AI chatbot, it is critical to integrate this human feedback to evaluate prompt responses. One common approach is to engage a diverse group of testers who grade prompts on a scale based on accuracy, and flag any harmful responses that may be biased or incorrect. These insights help fine-tune a chatbot, reducing safety issues and elevating user satisfaction.
2. Red Teaming
An essential element for testing GenAI is borrowed from the world of cybersecurity. Red teaming involves a proactive and adversarial approach to testing that simulates real-world attacks to uncover biases and vulnerabilities. GenAI red teaming challenges AI models with variable prompts (both offensive and safe) and probes for weak or inaccurate responses. By proactively identifying issues with red teaming, companies can place guardrails within a GenAI model, protecting users from potentially harmful content and eliminating safety and security issues.
3. Pre-Launch Testing
An organization must be fully satisfied of the chatbots’ readiness for real-world scenarios before the launch. This is why, in the lead up to a major release, the bots must undergo a final round of testing involving users from different geographies and demographics using a wide range of devices. This is often referred to as a trusted tester program (TTP) and has been shown to increase products’ Net Promoter Score (NPS).
Human Expertise is Essential
Responsible development and deployment of GenAI applications comes down to testing, and incorporating human experience and expertise mitigates risk and increases user satisfaction. Thankfully, leaders in GenAI development understand this, and are well on the way to unlocking high-quality, accurate and safe GenAI experiences through testing with real humans.
