Skip to content

Product Principles for AI Applications

Hello there! Jason here. After spending these chapters together exploring the world of RAG systems, I want to make sure you walk away with more than just technical knowledge. While the code examples and architectures are valuable, the real lessons I hope you've learned go much deeper.

The Flywheel Mindset

If there's one concept I want permanently etched in your mind, it's the improvement flywheel. Throughout my career—from Facebook to Stitch Fix to my consulting work—I've seen the same pattern: teams that build systems that get better with use succeed, while those that build static implementations eventually fail.

Your RAG application should be smarter next month than it is today. If it isn't, something is wrong with your process, not your technology.

Stop Guessing, Start Measuring

I've watched too many brilliant engineers waste countless hours debating which embedding model or chunking strategy is "best" without ever defining how they'd measure "best" in the first place.

Don't fall into this trap. Before you change anything in your system, know exactly how you'll measure the impact of that change. Without this discipline, you're just accumulating technical debt while pretending to make improvements.

Users Over Models

The most sophisticated RAG system that doesn't actually solve user problems is worthless. Period.

I've built systems that generated millions in revenue using outdated models because they solved real problems well. And I've seen state-of-the-art implementations fail because they missed the mark on user needs.

When in doubt, talk to your users. Read their feedback. Watch them use your system. This will teach you more than any research paper or GitHub repository ever could.

Specialization Beats Generalization

The path to exceptional RAG isn't finding the single best approach—it's identifying the different types of queries your users have and building specialized solutions for each.

This principle applies everywhere: specialized embeddings outperform general ones, targeted retrievers beat one-size-fits-all approaches, and segmented generation strategies outshine monolithic prompts.

Data Compounds Like Interest

In the early days of any RAG application, progress feels slow. You're manually creating synthetic queries, writing evaluation examples, and fine-tuning with limited data.

Don't get discouraged. Every piece of data you collect now becomes the foundation for automated improvements later. The first hundred examples are the hardest—after that, your flywheel starts spinning faster with each cycle.

Methods Matter More Than Models

Models will change. What was state-of-the-art when I wrote this will likely be outdated by the time you're reading it.

But the methods for systematic improvement are timeless. The processes for collecting feedback, evaluating performance, identifying patterns, and prioritizing improvements will serve you regardless of which models you're using.

The Hardest Problems Aren't Technical

In my experience, the biggest challenges in building successful RAG applications rarely involve model selection or hyperparameter tuning. They're about:

  • Convincing stakeholders to invest in measurement infrastructure
  • Getting users to provide meaningful feedback
  • Prioritizing improvements when resources are limited
  • Balancing quick wins against long-term architectural needs

The skills to navigate these challenges are as important as your technical abilities.

Start Small, But Start Now

You don't need a perfect RAG implementation to begin this journey. You don't need millions of examples or custom-trained models. You can start with a basic retriever, a few dozen synthetic queries, and simple thumbs-up/down feedback.

What matters is establishing the process for improvement from day one. Even a basic system that improves systematically will eventually outperform a sophisticated system that remains static.

Building a Culture of Continuous Improvement

Beyond the technical aspects, successful RAG products require the right organizational culture:

  • Celebrate learning over correctness: Teams that view failures as learning opportunities improve faster than those focused on being right the first time.

  • Share ownership of metrics: When everyone from engineers to product managers to business stakeholders aligns on key metrics, improvement accelerates.

  • Make feedback visible: Surface user feedback and performance metrics in dashboards, team meetings, and planning sessions to keep improvement central to your work.

  • Budget for refinement: Explicitly allocate resources for post-launch improvement rather than moving the entire team to the next project.

  • Document your journey: Keep records of what you've tried, what worked, and what didn't. This institutional knowledge becomes invaluable as your team grows.

Remember, this field is still young. The techniques we've covered are just the beginning. As you continue your journey, you'll discover new approaches and face unique challenges. But if you take these core principles to heart, you'll have the foundation to adapt and thrive regardless of how the technology evolves.

Build systems that learn. Measure before you change. Put users first. Specialize where it matters. Trust the process.

I can't wait to see what you build.

– Jason