Europe 2026
Feedback Loops are All You Need — Mehedi Hassan, Granola
Overview
This talk argues that effectively integrating AI into products requires more than just one-shot prompting. The core thesis is that building robust feedback loops, enabling detailed tracing, and creating flexible development environments are crucial for iterating on AI features and ensuring they meet user needs. This approach allows for a more magical user experience rather than relying on hope and guesswork.
Who should watch
- Product Engineers
- AI Engineers
- PMs
- Builders experimenting with LLM integrations
- Teams struggling with LLM output quality and control
- Developers working with desktop applications and CI/CD
Key takeaways
- Generic LLM chatbots often fail due to a lack of specific use-case molding, leading to issues like slow web searches or irrelevant responses.
- Integrating tools like web search can incur significant token costs and are subject to unpredictable model updates, impacting user experience and feasibility at scale.
- LLM output needs to be tailored to specific user roles (e.g., sales, engineering, HR), as a single prompt cannot satisfy diverse requirements.
- Building internal tracing tools provides essential visibility into LLM decision-making, tool calls, and costs, helping to debug and improve performance.
- For desktop applications, transforming the front-end into a web shell deployed online can significantly speed up development and testing by enabling easier preview links and parallel testing.
- AI can be leveraged within the development pipeline for self-verification, such as agents testing code changes and uploading screenshots to pull requests.
- The key to successful AI product development lies in creating a tight feedback loop, akin to a tennis match with the LLM, to ensure features resonate with users.
- Abstracting IPC and React APIs to fall back to web standards can make an Electron app's render process agnostic, allowing it to run as a web app.
Notable quotes
*The answer isn't to one-shot better. It's about figuring out how you can make that feedback loop where it kind of feels like playing a tennis game with LLM.*
*The end product feels more like magic rather than just like a black box and hoping that the feature that you're going to release works well with customers.*
Unofficial community note. Prefer the recording for nuance.