AWS offers some best practice advice for organisations wanting to implement generative AI (GenAI) solutions.
Begin with internal applications
It’s best to start generative AI adoption with internal application development, focusing on process optimisation and employee productivity.
You get a more controlled environment to test outcomes while building skills and understanding of the technology. You can test the models extensively and even customise them on internal knowledge sources.
This way, your customers have a much better experience when you do eventually use the models for external applications.
Enhance transparency
Clearly communicate about all generative AI applications and outputs, so your users know they are interacting with AI and not humans. For instance, the AI can introduce itself as AI, or AI-based search results can be marked and highlighted.
That way, your users can use their own discretion when they engage with the content. They may also be more proactive in dealing with any inaccuracies or hidden biases the underlying models may have because of their training data limitations.
Implement security
Implement guardrails so your generative AI applications don’t allow inadvertent unauthorised access to sensitive data. Involve security teams from the start so all aspects can be considered from the beginning. For example, you may have to mask data and remove personally identifiable information (PII) before you train any models on internal data.
Test extensively
Develop automated and manual testing processes to validate results, and test all types of scenarios the generative AI system may experience. Have different groups of beta testers who try out the applications in different ways and document results. The model will also improve continuously through testing, and you get more control over expected outcomes and responses.