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🤖🏗️ AI Engineering: Building Applications with Foundation Models

🛒 AI Engineering: Building Applications with Foundation Models. As an Amazon Associate I earn from qualifying purchases.

📝🐒 Human Notes

  • 🧠 2 Theories for hallucination
      1. 😵‍💫 Can’t differentiate between data given and data generated
      • 👨‍🔬 Ortega et. al
      • 🤥 self delusion
      • ❄️ snowballing hallucinations based on initial wrong assumptions
      • 🛡️ mitigation
        • 🏋️ reinforcement learning - differentiation about user provided prompts and tokens generated by model
        • 👨‍🏫 supervised learning - include factual and counterfactual signals in training data
      1. 🙅‍♂️ mismatch between model and labeler internal knowledge
      • ❓ if labelers answer questions based on facts unknown by model, models learn to hallucinate
      • 📝 if labelers state assumltions
    • 🤔 John Schulman believes that models know when they know something
      • ✅ verification - ask model to verify it’s output
        • 🗣️ prompt mitigations
          • 💯 answer as truthfully as possible
          • 🤷 say idk if you don’t know
          • ✍️ ask for concise responses
      • 🏋️ reinforcement learning - punish hallucinations
        • 🤖 RLHF might make hallucinations worse, but humans might prefer this. 🧑‍💻
  • 🤖 Prompt engineering best practices
    • ✍️ write clear, explicit, unambiguous instructions
    • 🎭 ask the model to adopt a persona
    • 💡 provide examples
    • 📌 specify the 📤 output format
      • 📝 e.g. no preambles
    • 🌍 provide sufficient context
    • 🧠 to restrict model knowledge to context
      • ❗ “answer using the only provided context”
      • 📚 cite your sources
    • 🧩 break complex tasks into simpler subtasks
    • 🧠 Let it think
    • 🔗 Chain of Thought (COT)
      • 🤔 “Think step by step”
      • 🗣️ “Explain your decision”
    • 🔎 Self Critique
      • ✅ “Check your work”
    • 🔄 Iterate
    • 2️⃣ Version your prompts
    • 📏 Evaluate prompt engineering tools
  • 📚 RAG
    • 🔑 Keyword vs ➡️ vector search
  • 🤖 Agents
    • 🧰 tool use
    • 🗺️ planning
    • ⚠️ failure modes
    • ✅ evaluation
  • 🧠 Memory
    • 🧠 Model contains internal knowledge
    • 🪟 Context window ~ ⌛ short term memory
    • 💾 External storage ~ 📆 long term memory

📚 Book Report: AI Engineering: Building Applications with Foundation Models

AI Engineering: Building Applications with Foundation Models by Chip Huyen 👨‍💻 serves as a comprehensive guide to the evolving field of AI engineering. ⚙️ It focuses on the practical application of large-scale, pre-trained models to build real-world applications. 🌍 The book is lauded for its systematic, end-to-end approach, guiding readers from the initial idea💡 to deployment and monitoring. 📊 A key theme is the shift from traditional machine learning, where models are often built from scratch, to a new paradigm that leverages the power of foundation models. 🚀 This makes AI application development more accessible and product-focused. 🎯

🔑 Key Topics Discussed

  • 🧠 Fundamentals of Foundation Models: The book introduces core concepts like masked language models, autoregressive language models, and self-supervision. 🤖 It explains the architecture of these models and the parameters that differentiate them. 🧮 The importance of data quality and quantity for training these models is also a significant point of discussion. 📈
  • 📊 Model Evaluation: A substantial portion of the book is dedicated to evaluating different AI models. ⚖️ It presents various metrics and even explores using AI for the evaluation process itself, with the goal of selecting models based on objective criteria. ✅
  • 💻 AI Implementation: The text delves into practical implementation strategies. 👨‍💻 “Prompt engineering” is covered as a method to influence model output without altering the model itself. 🗣️ Other techniques discussed include few-shot learning and prompt decomposition. ✂️
  • ⚙️ Fine-Tuning and Optimization: For more specialized tasks, the book explains the process of “fine-tuning,” which involves adjusting a model’s internal parameters to enhance its performance on a specific job. 🎯 It also covers dataset engineering and inference optimization to improve the efficiency of AI systems. ⚡
  • 🏗️ System Architecture: The final chapters tie everything together by illustrating the architecture of an AI system. 🧩 Huyen explains how to integrate the various components and collect user feedback for continuous improvement. 👂

🎯 Target Audience

This book is primarily aimed at a technical audience, including AI engineers, software developers, and machine learning practitioners. 🧑‍💼 While the content can be challenging for those without a technical background, it provides immense value for professionals actively building AI applications. 🛠️ The emphasis on practical application and real-world use cases makes it a valuable resource for anyone looking to move from AI prototypes to production-ready systems. 🏭

💡 Noteworthy Aspects

A distinguishing feature of this book is its intentional lack of extensive code snippets. 🚫 The author, Chip Huyen, focuses on providing a lasting conceptual framework that transcends specific and rapidly evolving APIs and tools. 🛠️ This approach emphasizes a product-oriented mindset in AI engineering, a perspective often overlooked in more technically focused literature. 📚

📚 Related Book Recommendations

🧑‍💻 Similar Reads (Focus on AI/ML Engineering)

🔍 Contrasting Perspectives (Broader AI Topics)

  • 🏥 The AI Revolution in Medicine: GPT-4 and Beyond by Peter Lee, Carey Goldberg, and Isaac Kohane: This book offers a contrasting perspective by focusing on the application of large language models in a specific domain, healthcare, highlighting the unique challenges and opportunities. ⚕️
  • 🌍 The Age of AI: And Our Human Future by Henry A. Kissinger, Eric Schmidt, and Daniel Huttenlocher: This work provides a high-level, strategic perspective on the societal and geopolitical implications of artificial intelligence, moving beyond the technical details of engineering. 🌐
  • 🤖⚠️📈 Superintelligence: Paths, Dangers, Strategies by Nick Bostrom: This book delves into the philosophical and existential questions surrounding advanced artificial intelligence, offering a stark contrast to the practical, application-focused nature of “AI Engineering.” 🤯

🎨 Creative Connections (Adjacent & Inspiring Reads)

  • 🐦‍🔥💻 The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win by Gene Kim, Kevin Behr, and George Spafford: While a work of fiction, this book’s narrative about improving IT processes and fostering collaboration holds valuable lessons for managing complex AI engineering projects. 🤝
  • 🤔🐇🐢 Thinking, Fast and Slow by Daniel Kahneman: Understanding the cognitive biases and mental shortcuts inherent in human thinking can provide valuable insights for designing more effective and user-friendly AI systems. 💡
  • 🌟 The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos: This book explores the different paradigms of machine learning and the quest for a single, universal learning algorithm, offering a broader context for the development of foundation models. 🔭

💬 Gemini Prompt (gemini-2.5-pro)

Write a markdown-formatted (start headings at level H2) book report, followed by a plethora of additional similar, contrasting, and creatively related book recommendations on AI Engineering: Building Applications with Foundation Models. Be thorough in content discussed but concise and economical with your language. Structure the report with section headings and bulleted lists to avoid long blocks of text.

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