For developers learning AI application engineering, this article maps the complete RAG pipeline from ingestion, chunking, retrieval, reranking, and generation to evaluation and operations, and presents an evolution path from a minimal viable solution to production.
Drawing on benchmarks and industry practice from multiple organizations, this guide presents default RAG chunking settings, parameter-tuning methods, and strategies for different document types.
A long-form Prompt Engineering guide for AI application engineers, covering foundational principles, context design, task chains, injection defenses, agent prompt design, and evaluation-driven development.
Starting from Anthropic’s Contextual Retrieval article and Appendix II, these notes summarize the core method, experimental findings, and RAG architecture principles suitable for production.
A curated list of ten high-value articles on Contextual Retrieval, Context Engineering, and RAG evaluation, spanning Anthropic’s original publications, open-source implementation guides, and a review of 2025 trends.