#NOTION AI CODE#

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.

#NOTION AI CODE#

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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