The rise of foundation models forces a shift in how teams build production AI: more emphasis on context construction, evaluation, and application patterns than on training from scratch. This repository collects companion materials that illuminate those engineering decisions—so you learn how to evaluate trade-offs and choose pragmatic paths for real systems, not just follow code snippets.
What Sets It Apart
- Focus on engineering decisions, not tutorials: materials prioritize frameworks and evaluation strategies (hallucination mitigation, RAG, agents, feedback loops) over line-by-line implementation. That helps teams reason about whether to fine-tune, build retrieval, or design an agent for a given product.
- Case-study driven insight: includes multiple real-world case studies and chapter summaries that distill lessons from production projects, making it easier to map concepts to practical trade-offs (accuracy vs. latency vs. cost).
- Lightweight tooling and examples: contains prompt examples, study notes, and small utilities (e.g., a ChatGPT/Claude conversation heatmap notebook) to reproduce analyses rather than full-stack deployments.
Who It's For and Tradeoffs
Great fit if you are an AI/ML engineer, technical product manager, or engineering leader who needs a principled playbook for adapting LLMs/LMMs to applications. Use it to learn evaluation methods, construct feedback loops, and compare RAG/finetuning/agent strategies. Look elsewhere if you need hands-on deployment templates, extensive codewalks, or a tutorial that teaches model training from first principles—this repo intentionally emphasizes conceptual guidance and curated references over exhaustive implementation details.
Where It Fits
Treat this repo as the companion notes and supplementary materials to the O'Reilly book. If you want actionable engineering judgment about when and how to apply foundation models in production, these resources accelerate decision-making; for turnkey infra or SDKs, combine these concepts with specialized repos and provider docs.
