The Stack Overflow Podcast
How AI apps are like Google Search
Jan 3, 2025
In this discussion, Daniel Loretto, CEO of Jetify, shares his journey from tech fascination to startup founder. He explores the complex relationship between AI applications and Google Search, focusing on predictability and user interaction. Daniel contrasts deterministic systems with probabilistic large language models, highlighting the importance of fine-tuning for reliability. He also discusses the future of AI agents in automating QA testing, allowing engineers to focus on creativity rather than tedious tasks. Finally, he touches on ClickOps and the value of documenting AI processes.
23:03
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Podcast summary created with Snipd AI
Quick takeaways
- Both AI applications and Google Search rely on user inputs to generate unpredictable outputs, necessitating better monitoring tools for developers.
- Establishing data tracing systems for user interactions in AI apps can enhance transparency and inform improvements, mirroring practices from Google Search.
Deep dives
The Evolution of AI and Search Systems
AI applications and Google Search share a critical characteristic of operating as data-driven systems, making their behavior hard to predict based on logic alone. Historically, Google Search relied on a sophisticated rule-based system before the advent of large language models (LLMs) and deep learning. Both systems depend on user inputs and generate outputs that can vary significantly, leading to a level of non-determinism in their responses. Consequently, developers need to implement tools that allow them to monitor and understand these processes better, ensuring outcomes align with user expectations.
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