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Episode Synopsis
AI Engineering: Building Applications with Foundation Models (Chip Huyen)
- Amazon USA Store: https://www.amazon.com/dp/B0DWHRL19D?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/AI-Engineering%3A-Building-Applications-with-Foundation-Models-Chip-Huyen.html
- Apple Books: https://books.apple.com/us/audiobook/die-with-zero/id1602704583?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=AI+Engineering+Building+Applications+with+Foundation+Models+Chip+Huyen+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- : https://mybook.top/read/B0DWHRL19D/
#AIengineering #foundationmodels #retrievalaugmentedgeneration #LLMevaluation #MLOps #AIEngineering
These are takeaways from this book.
Firstly, Product first thinking and problem framing, The book starts with the product, not the model. It teaches teams to define user jobs to be done, success metrics, and guardrails before touching prompts. You learn to decompose ambiguous AI ideas into narrow tasks with clear inputs, outputs, and constraints. Huyen emphasizes designing user interfaces that expose model uncertainty, enable correction, and capture feedback for continuous improvement. She covers human in the loop patterns such as review queues, approval flows, and confirmation steps that reduce risk without killing velocity. The chapter highlights common failure modes such as brittle prompts that do not generalize, features that lack a control group, and metrics that cannot be measured in production. You leave with templates to define scope, acceptance criteria, and evaluation plans so engineering effort focuses on impact. By anchoring on the user problem and measurable outcomes, teams avoid overfitting to demos and build features that endure.
Secondly, Retrieval augmented generation and data pipelines, RAG is presented as a system, not a single component. The book explains how to build a robust pipeline from data ingestion and cleaning to chunking, embedding, indexing, and query orchestration. It compares embedding models, distance metrics, and hybrid retrieval that blends dense vectors with keyword or metadata filters. You learn practical chunking strategies, citation tracking, freshness policies, and how to prevent leakage of outdated or restricted content. Huyen details ranking and fusion patterns, rerankers, and prompt orchestration that stitches retrieved context into model calls. She provides evaluation methods for RAG such as coverage, grounding accuracy, and answer faithfulness, along with canary datasets and synthetic probes. The chapter also covers caching, precomputation, and feedback loops that transform user interactions into better indices over time. You get recipes to handle multilingual corpora, long documents, and personal data with compliance in mind.
Thirdly, Model selection and adaptation strategies, Instead of one best model, the book proposes a portfolio approach. It shows how to choose between hosted APIs and self hosted models based on latency, cost, privacy, and customization needs. Huyen walks through instruction design, few shot examples, tool use, and constrained decoding to align outputs with business rules. For deeper adaptation, she compares fine tuning methods such as adapters and low rank updates, and explains when fine tuning beats prompt engineering or RAG. Topics include preference optimization, distillation to smaller models for cost control, and multimodal pipelines that combine text, vision, and audio. You learn to run experiments that isolate the impact of each change, avoid data contamination, and maintain reproducible prompts and model versions. The chapter ends with routing and fallback strategies across multiple models to balance quality and spend while...
- Amazon USA Store: https://www.amazon.com/dp/B0DWHRL19D?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/AI-Engineering%3A-Building-Applications-with-Foundation-Models-Chip-Huyen.html
- Apple Books: https://books.apple.com/us/audiobook/die-with-zero/id1602704583?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=AI+Engineering+Building+Applications+with+Foundation+Models+Chip+Huyen+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- : https://mybook.top/read/B0DWHRL19D/
#AIengineering #foundationmodels #retrievalaugmentedgeneration #LLMevaluation #MLOps #AIEngineering
These are takeaways from this book.
Firstly, Product first thinking and problem framing, The book starts with the product, not the model. It teaches teams to define user jobs to be done, success metrics, and guardrails before touching prompts. You learn to decompose ambiguous AI ideas into narrow tasks with clear inputs, outputs, and constraints. Huyen emphasizes designing user interfaces that expose model uncertainty, enable correction, and capture feedback for continuous improvement. She covers human in the loop patterns such as review queues, approval flows, and confirmation steps that reduce risk without killing velocity. The chapter highlights common failure modes such as brittle prompts that do not generalize, features that lack a control group, and metrics that cannot be measured in production. You leave with templates to define scope, acceptance criteria, and evaluation plans so engineering effort focuses on impact. By anchoring on the user problem and measurable outcomes, teams avoid overfitting to demos and build features that endure.
Secondly, Retrieval augmented generation and data pipelines, RAG is presented as a system, not a single component. The book explains how to build a robust pipeline from data ingestion and cleaning to chunking, embedding, indexing, and query orchestration. It compares embedding models, distance metrics, and hybrid retrieval that blends dense vectors with keyword or metadata filters. You learn practical chunking strategies, citation tracking, freshness policies, and how to prevent leakage of outdated or restricted content. Huyen details ranking and fusion patterns, rerankers, and prompt orchestration that stitches retrieved context into model calls. She provides evaluation methods for RAG such as coverage, grounding accuracy, and answer faithfulness, along with canary datasets and synthetic probes. The chapter also covers caching, precomputation, and feedback loops that transform user interactions into better indices over time. You get recipes to handle multilingual corpora, long documents, and personal data with compliance in mind.
Thirdly, Model selection and adaptation strategies, Instead of one best model, the book proposes a portfolio approach. It shows how to choose between hosted APIs and self hosted models based on latency, cost, privacy, and customization needs. Huyen walks through instruction design, few shot examples, tool use, and constrained decoding to align outputs with business rules. For deeper adaptation, she compares fine tuning methods such as adapters and low rank updates, and explains when fine tuning beats prompt engineering or RAG. Topics include preference optimization, distillation to smaller models for cost control, and multimodal pipelines that combine text, vision, and audio. You learn to run experiments that isolate the impact of each change, avoid data contamination, and maintain reproducible prompts and model versions. The chapter ends with routing and fallback strategies across multiple models to balance quality and spend while...
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