Why Notion’s biggest AI breakthrough came from simplifying everything

Source: VentureBeat

Introduction

When initially experimenting with LLMs and agentic AI, software engineers at Notion AI applied advanced code generation, complex schemas, and heavy instructioning. Quickly, though, trial and error taught the team that it could get rid of all of that complicated data modeling. Notion’s AI engineering team discovered that simplifying the problem space yielded far better results.

The Early Approach

• Advanced code generation: The team tried to generate sophisticated code snippets on the fly.
• Complex schemas: They built intricate data structures to capture user intent.
• Heavy instructioning: Prompts were loaded with detailed, multi‑step instructions.

These tactics seemed promising at first, but they introduced latency, brittleness, and a steep learning curve for both developers and end‑users.

The Turning Point

Through iterative testing, the engineers realized that many of the elaborate components were unnecessary. By stripping away the layers of abstraction and focusing on a more straightforward interaction model, they achieved:

1. Reduced latency – fewer processing steps meant faster responses.
2. Improved reliability – a simpler system had fewer failure points.
3. Better user experience – users could interact with the AI without needing to understand complex schemas.

The Simplified Solution

The team pivoted to a minimalist design:

• Flat data structures: Instead of nested schemas, they used flat key‑value pairs.
• Concise prompts: Prompts were rewritten to be short, clear, and context‑aware.
• Iterative refinement: The AI was allowed to ask follow‑up questions rather than trying to infer everything upfront.

This approach let the LLM focus on core language understanding tasks, while the surrounding infrastructure handled context management in a lightweight manner.

Results

• Higher accuracy: The AI’s output quality improved despite (or because of) the reduced complexity.
• Scalability: The streamlined architecture scaled more efficiently across Notion’s user base.
• Developer productivity: Engineers spent less time maintaining convoluted code and more time building new features.

Takeaways

• Simplicity can outperform complexity when working with large language models.
• Iterative testing is crucial; assumptions about “more advanced” solutions may be misleading.
• User‑centric design—focusing on what users actually need—leads to better product outcomes.

By embracing simplicity, Notion AI turned a challenging engineering problem into a competitive advantage, demonstrating that sometimes the biggest breakthroughs come from removing, not adding, layers of complexity.