bagrounds.org

4 min read

Home > Videos | 🤖🏗️ AI Engineering: Building Applications with Foundation Models

🤖🧠⚙️👩‍💻 AI Engineering with Chip Huyen

🤖 AI Summary

✨ AI engineering focuses on 🚀 product development and leveraging 💡 existing AI capabilities through API calls, rather than building models from scratch [13:55]. This ⬇️ lowers the entry barrier, reducing the need for extensive data or specialized AI degrees [12:59].

Typical development path for AI applications:

  • 🎯 Understanding user needs: It is crucial to first 📝 define what constitutes a “good” or “bad” response from the AI by understanding user expectations and feedback [19:18]. For example, LinkedIn’s job fit assessment initially focused on correctness, but shifted to providing 🛠️ helpful feedback and suggestions for improvement to candidates [19:34].
  • ✍️ Prompt engineering and examples: Begin with well-defined prompts and gradually ➕ add more examples to improve output [20:30].
  • 📊 Evaluation: Establish clear guidelines and use both 🤖 automated metrics and 🧑‍🤝‍🧑 human evaluation to measure progress [20:41].
  • 📚 Context augmentation (RAG): For more complex queries, provide the model with 📖 additional context by retrieving relevant documents or information [21:06]. Starting with simpler methods like keyword retrieval is emphasized before jumping to more complex and potentially expensive vector databases, highlighting that data preparation often yields greater performance boosts [22:47].
  • ⚙️ Fine-tuning: This is considered a last resort due to the complexities of hosting, maintaining, and the rapid pace of new model releases that can quickly 📈 outperform fine-tuned models [25:27].

Common mistakes: Using generative AI when simpler solutions suffice or giving up on it due to poor implementation rather than inherent limitations [49:53]. The importance of understanding the core problem and applying 🧩 systematic approaches rather than blindly adopting shiny new technologies is stressed [33:56].

Future potential of AI: In 🎓 education, enabling faster and more effective learning by helping users formulate the right questions [01:05:11]. In 🎬 entertainment, creating intellectually stimulating and adaptable content across various mediums [01:06:26]. AI will 💻 automate coding, allowing software engineers to tackle more complex problems and systems [01:03:58].

🤔 Evaluation

This discussion offers a 💡 pragmatic view of AI engineering, emphasizing product development and strategic application over foundational model building. While it highlights the accessibility of AI through APIs, it implicitly contrasts with the deeper technical expertise required for AI research and core model development. A topic to explore for a better understanding is the ⚖️ ethical implications and biases embedded within pre-trained AI models, which are consumed via APIs. Additionally, understanding the 🔒 security considerations when integrating external AI services would be beneficial.

📚 Book Recommendations

  • Working with AI: A practical guide for leaders, managers, and individual contributors by Thomas H. Davenport and Steven M. Miller: This book offers guidance on how to effectively integrate AI into organizations, aligning with the video’s focus on practical AI application.
  • 🤖⚙️🔁 Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen: As the author of the video’s discussed “AI Engineering” book, this provides a deeper dive into the technical aspects of building and deploying ML systems, offering a more in-depth perspective on the practicalities of AI engineering.
  • The Ethical Algorithm: The Science of Socially Responsible Algorithm Design by Michael Kearns and Aaron Roth: This book explores the challenges of fairness, accountability, and transparency in algorithms, which is crucial for understanding the broader societal impact of AI systems mentioned in the video.
  • Grokking Machine Learning by Luis Serrano: For those new to machine learning, this provides a fundamental understanding of core concepts, which can help in appreciating the underlying principles of the AI capabilities being leveraged through APIs.
  • 🤔🐇🐢 Thinking, Fast and Slow by Daniel Kahneman: While not directly about AI, this book explores human cognitive biases and decision-making, offering insights into how human evaluation (as mentioned in the video) can be influenced and how AI systems might learn to emulate or mitigate such biases.

🐦 Tweet

Graph View

Backlinks