Generative AI fundamentally changes what it looks like to build software products. If you're using an LLM in your product, you're building an ML system whether you like it or not. 😎 And need a process to go with it. The team Deloitte were kind to invite me to share some learnings in their Deloitte Insights publication. This post is targeted at technology leaders who are newer to the AI space, but hopefully a helpful read for anyone. Check it out. 👇
Our CEO Ian Cairns was invited to contribute to Deloitte Insights for technology leaders. He talks about what's important to know about building with generative AI, and what makes software teams successful. Some of the big ideas, and link in the comments: 📈 Evaluation: "A custom panel of contextually relevant evaluations forms the backbone of analyzing AI products." 🧑💻 Data labeling and curation: "You need people with sufficient domain expertise constantly looking at data. There’s no such thing as full automation when it comes to building great generative AI products." 🧪 Testing: "Testing a generative AI product requires coming up with a representative list of all the possible types of interactions and edge cases that may occur for customers, and making sure each behaves reasonably." 🤷 Why bother? "Generative AI will be a huge competitive advantage for companies, but only if they’re able to make the jump to operate successfully in these new ways. The folks who haven’t yet stepped into that process change often find themselves stuck experimenting and trying to get the confidence they need to even ship to production." What resonates? What did he miss? Tell us in the comments.
Interesting stuff! Thanks, Ian.