Notion clearly began with customer pain points in mind. Notion's AI product are great examples of this process. It should be pretty clear which problems AI can solve, and which would be easiest to build-start with those! Start by holding a session and putting the list of pain points to solve and the list of AI capabilities side by side. Some ways that companies have leveraged these effectively include: The tricky part is that these are very general capabilities, which means they can be applied to almost any application. There are a variety of tasks that generative AI excels in, but a few areas where it is particularly effective are: Once you have a list of customer pain points, you should evaluate which are reasonable pain points for generative AI to solve. If that fails, then customer interviews or a customer survey could do the trick. Your product and sales teams could probably name a few off the top of their head. There’s no one right way to discover problems or pain points. Your journey to great AI features begins by finding an acute customer pain point. Step 1: Identify a customer pain point that generative AI can fix Fortunately, we can learn from the best-and here’s how they’ve done it. This means that companies have to think very, very differently about the way they build AI applications. The AI apps that have performed extremely well showcase the fact that there’s an assistant working for you-it’s not buried. The “AI” part of the app is no longer hidden, it’s the star. ![]() And you can build a proprietary model over time. Unless you’re a company with very specific needs around cost or latency, proprietary models will serve you well. Most of the “wow factor” from an AI feature will come from changes around the model (i.e., the data used in the vector DB, or prompt engineering) rather than improvements to the model itself. Small improvements in model performance will no longer give you a big improvement in application performance. Any engineer can build a compelling AI feature with an API call, a vector database, and some tweaks to a prompt. Off-the-shelf proprietary models are incredibly performant for company-specific applications with a few, minor tweaks. They didn’t represent new products / features themselves. Small performance improvements made a big difference for a ranking algorithm or search engine, so teams of AI/ML engineers spent a lot of time focusing on incremental improvements.ĪI/ML was largely hidden: These models augmented features that were built with descriptive code (e.g., Facebook’s newsfeed, Netflix’s video content). Small improvements in model performance made a big difference: Building a more performant model for a specific use case represented the lion’s share of the AI/ML work work. Because of this, building a great model was a big edge. ![]() The model was the edge: It was very effort intensive to build a model that worked well in a given application, and it a very rare skillset. The last generation of AI/ML features were defined by three things: They were the only companies that could afford to pay top dollar for whole teams of AI/ML engineers, so their models gave them an edge. ![]() These companies could rely on AI/ML as a point of differentiation because it was incredibly difficult and expensive to build performant models. AI/ML features were a key point of differentiation for the last generation of great tech companies-ML models were the backbone of Facebook’s ad platform, Google’s search engine, and Netflix’s recommendation engine. ![]() People have been building AI products for a long time. First, why building AI products today is different We'll bring this to life with examples from Notion - who built an amazing AI product, as a non-native AI company. Step 5: Continuously improve the product by experimenting with every input Step 4: Put this product in front of real users as soon as possible Step 3: Measure engagement, user feedback, cost, and latency Step 2: Build a v1 of your product by taking a propriety model, augmenting it with private data, and tweaking core model parameters Step 1: Identify a compelling problem that generative AI can solve for your users In short, here’s what it takes to build an amazing AI product if you’re not an AI company: We’ve seen a number of companies build, launch, and continuously improve AI products, and we have thoughts on the recipe for success. Where do you start? How do you build something great? You’ve read all the hype about Generative AI, you’ve seen amazing product demos from competitors, and your VCs are hounding you to incorporate AI into your product. You know it’s time to build an AI product. Imagine that you’re the CEO of a successful documents company.
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