As generative artificial intelligence tools such as ChatGPT, Claude, Gemini and others see increased use among professionals across industries, some organizations may consider building their own, specialized GenAI platforms for either their own or their customers’ use (or both). It’s an effort that can come with multiple benefits, including solutions tailored for a company’s specific industry and clients, the ability to better control sensitive data, better experiences and outcomes for users, and much more.
However, building a bespoke GenAI solution also comes with significant complications that organizations need to be aware of before they begin the process. Below, members of Forbes Technology Council discuss some of the challenges inherent in building specialized GenAI platforms and how they can be addressed.
When developing a specialized GenAI model for an industry that is heavily reliant on human domain expertise, such as manufacturing, striking a balance between expert feedback and AI is key. GenAI on its own can hallucinate. Organizations must pair GenAI with a library of domain data and AI algorithms that learn from expert feedback to avoid inaccuracies and drive reliable outputs. – Saar Yoskovitz, Augury
GenAI is top of mind, and for good reason. Productivity increases are a game-changer, but without good governance, disaster can occur. Access to data by these systems must be carefully monitored and controlled, and—just as important—user prompts should be too. Asking, “Show me addresses of single high-net-worth individuals in my zip code” is a red flag, and if data is returned, a big problem. – Devin Redmond, Theta Lake
Read the full article by Forbes.
