- Intelligent Products Insights
- Posts
- How to Build Products That Learn Over Time
How to Build Products That Learn Over Time
Most products are built to ship. Intelligent products are built to evolve.
Rode Esha
June 21, 2025
In today’s tech climate, shipping a product is no longer the finish line, it’s the starting line. The real winners are products that learn and improve with every interaction. Instead of a static tool that slowly becomes outdated, imagine a product that adapts like a living, breathing entity, getting smarter and more useful as people use it. In this issue, we’ll explore how to infuse that adaptive, feedback-driven DNA into your product. We’ll break down a model for continuous learning, look at a real case study of an intelligent product in the wild, and share a practical toolkit for building products that get better over time.
The Adaptive, Feedback-Driven Product (Deep Dive)
Traditional products follow a “build it and they will come” mindset ship features and hope users find value. Intelligent products, on the other hand, are built with a “learning loop” mindset – they continuously sense, learn from data, and adapt to meet users’ evolving needs. This requires a fundamental shift in product strategy and architecture:
Data as Foundation: Data is the lifeblood of an intelligent product. Without rich, high-quality data, even the best algorithms will fail (“garbage in, garbage out”). Successful products are designed from day one to capture the right data every user action, feedback, context and to store it in a unified, accessible way. This isn’t an afterthought; it’s a core design principle. For example, modern data architectures (cloud data lakes, warehouses) are set up to handle massive volumes of behavior data in real time, because the more quality data you feed the system, the smarter it gets.
Intelligence Layer (AI + BI + CI): Once data is in place, an intelligent product leverages multiple forms of intelligence:
AI (Artificial Intelligence) – the “brain” of the product that learns patterns and makes predictions or decisions. AI capabilities enable personalization, recommendations, anomaly detection, automation, and more.
BI (Business Intelligence) – the analytical insight into how the product and the AI are performing. This is like the product’s internal dashboard or feedback on itself. BI tracks metrics, identifies trends, and closes the loop on performance. It’s not just about dashboards for humans; it can also mean monitoring how AI models or agents are behaving and feeding that back into improvement.
CI (Customer Intelligence) – a deep understanding of the user’s context, behavior, and needs. This involves analyzing user behavior data, segmenting personas, and even gathering qualitative feedback. CI answers why users do what they do, helping the product experience to be more empathetic and user-centric.
These three intelligences can work independently, but the magic happens when they work in harmony . Think of it this way: AI might decide what to show or do (prediction), BI observes how well that decision worked (analytics), and CI provides the why (user context and feedback). Together, they form a virtuous cycle of learning.
Continuous Learning Loop: An intelligent product is never “finished.” It continuously iterates in a loop: it uses data to make a decision or provide an experience, observes the outcome (did the user engage? was the prediction accurate?), collects feedback (explicit or implicit), and adjusts its model or rules for next time. Every interaction is an opportunity to get better. This is often enabled by machine learning pipelines that retrain models on new data, or by rule-based systems that update based on feedback. The question guiding intelligent product design is, “How will this feature get better the more it’s used?” If you have a clear answer to that, you’re on the right track.
Human in the Loop: “Intelligent” doesn’t mean fully autonomous. The smartest products often incorporate human oversight and input at critical points. Whether it’s a content recommendation system that lets users thumbs-up/down suggestions, or an AI assistant that flags uncertainty for a human to review, involving humans ensures the learning doesn’t veer off course. For instance, OpenAI’s ChatGPT was fine-tuned with human feedback (RLHF) to align its behavior with user preferences – showing that even cutting-edge AI benefits from human guidance during training . Embracing a human-in-the-loop approach can catch failures early and inject judgement into the learning process.
Decentralized “Agentic” Architecture: A notable emerging trend is the move beyond monolithic AI systems toward multi-agent systems. Instead of one giant AI trying to do everything, you have specialized AI agents that each excel at a particular task (natural language, image recognition, routing a query, personalizing content, etc.), coordinated by an orchestration layer. This is akin to having a team of specialists rather than a single jack-of-all-trades. Such agents can even call on each other for example, one agent might analyze a customer query, then hand off to a second agent specialized in generating a personalized answer, and a third agent to analyze if the user was satisfied. The key is orchestration: designing how these agents communicate and collaborate. According to a recent Botpress report, “AI agent orchestration is the practice of coordinating multiple specialized AI agents each with a distinct role to work together toward a shared goal.” This modular approach scales better as complexity grows . We’re already seeing frameworks (e.g. OpenAI’s Agents SDK with Model Context Protocol) making it easier to build these agent ecosystems . For product builders, it means you can plug in new intelligence components or swap out models without rebuilding the whole system – future-proofing your product as AI tech evolves.
Security, Privacy, and Ethics by Design: An often overlooked element of “learning products” is that they typically handle sensitive data and automated decisions – a potent mix that demands strong governance. Build security and privacy into every layer from the start. Ensure data is anonymized where needed, models are tested for bias, and there are safeguards (and override options) for when the AI gets it wrong. Users will only embrace an evolving, data-hungry product if they trust it. Done right, ethical AI isn’t a speed bump – it becomes a competitive advantage.
Product Experience that Evolves: Finally, the user experience itself should communicate that the product is getting smarter and use that to delight users. Design the UI/UX to be context-aware and responsive. For example, a truly intelligent app might change what it shows on the home screen based on time of day or learned preferences. It might proactively assist the user (“You usually do X at this time – need a shortcut?”). Importantly, give users transparency and control: if the product makes an automated decision, explain (in simple terms) why, and let the user correct it if it’s wrong. An intelligent product builds trust by showing the user it’s learning with them, not just about them.
In short, building an intelligent product means combining solid data foundations with AI/ML capabilities, baking in continuous feedback loops, and crafting a flexible system architecture that can adapt and grow. Instead of a static product, you’re building a product organization – a system that observes, decides, and improves like a living organism. The payoff for this extra effort? A product experience that feels almost magical to users – one that anticipates needs, gets better each week, and ultimately creates a moat that’s hard for copycats to cross.
Real Use Case – Netflix: The $1B Learning Loop
To make these ideas concrete, let’s look at how Netflix built one of the world’s most famous intelligent product systems: its content recommendation engine. In the early 2000s, Netflix was essentially “Product 1.0” a DVD rental site with a simple recommendation feature based on what you rated. It was useful, but relatively static. Fast-forward to today, and Netflix’s personalized recommendation system is a prime example of a product that learns over time and drives massive business value.
How the Loop Works: Every time you use Netflix, dozens of algorithms are quietly at work. Initially, Netflix asks for your ratings to jump-start your profile (that was the idea behind their famous Netflix Prize competition). But the real learning happens through usage: what you watch, how long you watch, what you browse and don’t watch, the time of day, the device you’re on – all these signals feed back into Netflix’s models . There’s a continuous feedback loop:
You watch something (or not).
Netflix records that behavior as implicit feedback.
Overnight (or in real-time now), its algorithms retrain or adjust recommendations based on all that collective behavior data.
Tomorrow, the homepage recommendations and rankings adjust slightly for you and for users similar to you.
Crucially, Netflix doesn’t just rely on one algorithm. It uses a suite of models (collaborative filtering, content-based models that consider genres/actors, trending-now models, etc.) and even an ensemble of them to predict what you’re most likely to engage with . Over time, it has also learned to personalize how it presents choices – even the artwork thumbnails are chosen based on what the system thinks will catch your eye (for instance, showing you an action scene vs. a romance scene from the same movie, depending on your preferences).
Adapting to User Behavior: A hallmark of Netflix’s intelligent approach was recognizing that user tastes change and contexts vary. Maybe you normally love crime thrillers, but one lazy Sunday you’re in the mood for a goofy comedy. Netflix’s algorithms are designed to detect shifts and out-of-pattern behavior. If you break your usual pattern, it will still offer something left-field (often via the “Because you watched X” or the Top 10 row). It’s essentially exploring other options while still exploiting what it knows works – a classic explore/exploit balance to keep learning about you.
Results: The impact of this continuously learning product has been enormous. Netflix revealed that approximately 80% of the hours streamed on Netflix are driven by algorithmic recommendations, not direct searches . By taking the burden of choosing off the user’s shoulders, Netflix keeps people watching. In fact, the recommendation engine became so effective at engagement and retention that Netflix estimates it saves over $1 billion per year in value by reducing churn . That is, without the personalized experience holding attention, many more users would give up and cancel. Moreover, Netflix has achieved a remarkably low churn rate (~2-3%), partly credited to the “stickiness” of a homepage that instantly serves up something you’re likely to like .
It wasn’t always smooth sailing, there were bumps like the time Netflix tried to overhaul its recommendation algorithm after the Netflix Prize, only to find the new ensemble was too complex to justify implementing fully (they ended up blending some of those ideas into production gradually). They also learned the hard way about feedback loops reinforcing certain content; Netflix has to occasionally inject serendipity so the user doesn’t get stuck in a filter bubble of the same genre. And the introduction of user profiles (letting family members separate their viewing data) was a direct product decision to improve data quality for learning – it eliminated the “mixed signals” problem when one account’s history actually belonged to different people. These adjustments underscore a key point: intelligent products require ongoing tuning. It’s a continuous cycle of experiment -> measure -> learn -> adjust. Netflix runs thousands of A/B tests each year on its algorithms and UX, essentially letting the product itself tell them what’s working through data.
For anyone building products, Netflix’s journey drives home the value of the feedback-driven approach. The product you launch on day one doesn’t have to be perfect – if it’s instrumented well and designed to learn, it will become better almost automatically. Netflix couldn’t have known in 2009 what each of its 200+ million members want to watch in 2025 – but by building an intelligent system and gathering feedback at scale, it figures that out on the fly. Your product can do the same in its domain.
Intelligent Product of the Week: GitHub Copilot (AI Pair Programmer)
Each week, we spotlight an example of an “intelligent product” making waves. Today’s pick: GitHub Copilot.
What it is: GitHub Copilot is an AI-powered coding assistant, introduced in 2021 and now widely used by developers. It’s like having an “AI pair programmer” inside your code editor. As you write code, Copilot suggests the next line or even a whole function instantly, based on the context of what you’re working on. It’s powered by OpenAI’s Codex model (a descendant of GPT-3 fine-tuned on billions of lines of source code).
Why it’s intelligent: Copilot is a great example of a product built to learn and adapt in real time. It leverages the context (the code you’ve written in the file, your comments, the programming language and frameworks detected) to tailor its suggestions. The more context you give (for instance, a comment saying “function to sort list of users by name”), the better its suggestion can be. In essence, it’s learning from your specific codebase on the fly. Moreover, the Copilot team at GitHub continuously improves the underlying AI model by training on new data and incorporating user feedback (developers can manually accept or reject suggestions – which is feedback!). Over time, it’s getting better at understanding different coding styles and project conventions.
Impact: The productivity boost from this intelligent tool has been significant. In a controlled study, developers using GitHub Copilot were able to complete a coding task 55% faster than those without it . That’s a dramatic efficiency gain – what might have taken 2 hours took just about 1 hour with Copilot’s help. Beyond speed, it also helps reduce mental strain: routine code (like writing boilerplate or simple functions) is handled by suggestions, freeing developers to focus on more complex logic. GitHub’s surveys found that 73% of users felt they could stay in “flow” longer and avoid rote fatigue when using Copilot . It’s not writing whole apps at the click of a button – but it’s augmenting human developers in a very tangible way.
What we learn: GitHub Copilot illustrates a key intelligent product pattern: human-AI collaboration. The AI doesn’t operate alone; it works with the user. Copilot suggests, a human approves/edits – a tight feedback loop. Each acceptance or rejection is a signal to improve future suggestions for that context. It’s also a case where the product’s value grows with usage: as more developers use Copilot, GitHub gathers more data on which suggestions are useful, which ones lead to edits, and can refine the model (respecting privacy and not memorizing proprietary code, of course). In effect, the community of users collectively train the AI to be a better assistant.
Finally, Copilot’s design shows thoughtful integration into existing workflows. It didn’t ask developers to go to a separate AI tool; it came to them in VS Code, Neovim, JetBrains, etc. – meeting users where they work. That’s an underrated lesson for intelligent products: make the intelligence a seamless part of the user experience, not a flashy gimmick. When the AI feels like a natural extension of the product, users engage more, which generates more data… which creates a better product. (There’s that flywheel again!)
Pulse Poll: Your Experience
Cast your vote by clicking A, B, C, or D. (Feel free to elaborate on your answer we’ll feature interesting insights next time!) Poll result will be displayed after voting
Signals from the Field 🌐
Cutting-Edge Multi-Agent Systems: The era of a single AI doing it all is fading. Tools and frameworks for orchestrating multiple AI agents are emerging fast. OpenAI’s recently announced “Agents” SDK (with its Model Context Protocol) is one example, enabling a team of specialized AIs to collaborate on tasks. Why should you care? Multi-agent setups promise more flexibility and robustness. As one AI orchestration report put it: breaking a complex job into a team of narrow AI agents, coordinated by a controller, lets systems “scale across use cases and domains” without collapsing under complexity . In other words, your product can tackle more diverse tasks by assigning them to the best-fit AI agent, rather than stretching one model too far.
Personalization & Friction Spotify’s Secret: Spotify’s design team revealed that the success of their famous Discover Weekly playlist was not just about clever algorithms, but about removing friction for the user. Finding new music used to be work – Spotify made it effortless by delivering a fresh, tailored playlist every week. It’s a textbook intelligent product move: identify a user struggle (in this case, the time and effort to discover songs) and use data + AI to solve it in a delightfully automated way. As Spotify’s team noted, “Discover Weekly wasn’t successful simply because of its catchy name or cover art… It was successful because it automated the personal music expert; it made discovery effortless.” Within a year of launch, Discover Weekly had over 40 million users, and it’s still a flagship example of leveraging data-driven personalization at scale to win user loyalty.
AI Meets BI for Decision-Making: A growing trend in industry is blending AI into traditional business intelligence tools sometimes called “augmented analytics.” Modern BI dashboards are starting to include AI agents that automatically surface insights (“Hey, this metric is an outlier this week, and here are likely reasons”) rather than just displaying charts. Companies are increasingly applying AI to monitor their own operations data, not just user-facing features. The takeaway? Your product’s learning loop shouldn’t stop at the user interface it can extend behind the scenes, with AI helping your team make smarter decisions about the product. For example, monitoring algorithms can detect segments of users that are struggling and prompt a product intervention. Think of it as your product learning about itself. This internal loop, powered by AI and BI, is what turns good products into truly great, constantly-improving ones.
(Have you come across a compelling insight or stat in the data/AI/product world? Share it with us for a future “Signals” highlight.)
Intelligent Product Toolkit 🔒
A mini playbook – 5 Principles of Intelligent Product Design to guide you in building products that learn:
Data First, Feature Second – Before thinking about fancy features, ensure you’re capturing the right data exhaust from every interaction. Design data collection points thoughtfully (and ethically). Instrument everything relevant: user clicks, time spent, drop-offs, feedback clicks. You can’t improve what you don’t measure. Make data quality a priority (clean schemas, unique identifiers, unified definitions). A strong data foundation will pay dividends every time you refine your models or analysis.
Design the Feedback Loop – For each feature, explicitly plan how it will get better over time. Is there a rating, like/dislike, or usage signal that will inform the next iteration of content or behavior? Build mechanisms for users (or systems) to provide feedback, implicitly or explicitly. Even simple prompts like “Was this recommendation helpful? [Yes/No]” can generate invaluable training data. The best intelligent products bake the feedback loop into the core user flow, so learning happens naturally as a by-product of use.
Human-in-the-Loop at Critical Points – Don’t leave your AI on an island. Identify where human insight is most needed or valuable in the process and integrate it. This could be in curating training data (labeling a dataset or moderating outputs), in real-time oversight (e.g. flagging an AI decision for review if confidence is low), or in post-hoc analysis (examining model errors to guide improvements). Human input acts as a safety net and accelerant – it catches the AI when it stumbles and teaches it to do better. This principle will keep your product both intelligent and responsible.
Modular & Scalable Architecture – Build your product’s brain in a modular way. Instead of one monolithic AI model that’s hard to replace, use a set of models/agents for different tasks (as we discussed in multi-agent systems). Use APIs and microservice principles for your AI components so you can swap in improved versions or new techniques as they emerge. Scalability is also key: if your user base or data volume 10xs, can your learning pipelines and infrastructure handle it? Plan for horizontal scaling of data stores and model inference. Scalability ensures your intelligent features remain performant and responsive as you grow.
User-Centric Experience (Transparency & Control) – An intelligent product must still feel intuitive and trustworthy to the user. Design your UI so that the AI’s contributions are understandable. Show recommendations or automated actions in a way that users grasp why they’re seeing them (even if only subconsciously). Provide controls: let users correct recommendations (thumbs down, “not interested in this”), adjust preferences, or opt out of personalization if they choose. Transparency might mean showing a brief explanation: “Recommended because you liked __.” When users see the product adapting in a way that aligns with their desires, it builds a virtuous cycle of trust and engagement, which leads to more usage and more data to learn from.
One Thought to Leave You With
In the words of Google co-founder Larry Page, reflecting on what makes an AI-driven product truly special:
“Build a product that’s AI-complete… The product would get better, and because the product gets better, more people use it. That helps you to create more data for the AI to get better, and that makes the product better.”
It’s a simple idea with profound implications. The more your product can learn from its users, the stronger it gets – a self-reinforcing flywheel. As you build, keep asking yourself (and your team) that one essential question: How will this product get better the more it’s used? The answer will guide you to true intelligent product design.
What’s your favorite intelligent product or feature, and how has it impressed you? Hit reply and let me know I’d love to hear your take, and I might feature it in an upcoming issue.
Enjoyed this deep dive? Don’t keep it to yourself forward it to a friend or colleague interested in data, AI, or product innovation. Let’s grow the community of intelligent product builders!
Until next time, stay curious and keep building products that get smarter by the day.
Reply: What’s an intelligent product (and use case) you admire? Share your thoughts – I may feature your example in the next newsletter.
Vote: Don’t forget to vote in this week’s pulse poll above – your input matters.
Share: Found this newsletter useful? Forward it to someone who’s building with data, AI, or product in mind. Let’s learn and build smarter, together.
Sources: Netflix case – personalization impact ; Spotify design – Discover Weekly insight ; Multi-agent orchestration ; GitHub Copilot study ; Larry Page quote , and others.
Netflix’s Recommendation Engine – 80% of Views Driven by Algorithms
https://www.wired.com/2013/06/how-netflix-reverse-engineered-hollywood/
Netflix and the $1 Billion Impact of Personalization
https://www.forbes.com/sites/gilpress/2016/06/09/netflix-recommendations-worth-1-billion-per-year/
GitHub Copilot Productivity Study (55% Faster)
https://github.blog/2023-07-20-the-impact-of-github-copilot-on-developer-productivity-and-happiness/
GitHub Copilot Research: Developer Satisfaction & Flow
Spotify Discover Weekly – How Personalization Works
https://uxdesign.cc/spotify-discover-weekly-behind-the-playlist-that-knows-you-so-well-14eab6ac508c
OpenAI Agents SDK + Multi-Agent Protocol (MCP)
Botpress on AI Agent Orchestration
Larry Page on AI Flywheels and Intelligent Product Strategy
https://a16z.com/2023/08/07/ai-flywheels-how-intelligent-products-improve-themselves/
Augmented Analytics and the Future of BI + AI
OpenAI’s Reinforcement Learning from Human Feedback (RLHF)