Evaluation

Use this article as a reference when building an enterprise RAG system.

Drawing on Latent Space’s interview with Shunyu Yao, this article reviews ReAct, Reflexion, Tree of Thoughts, memory, benchmarks, ACI, and Agent UX, and summarizes the importance of tools, environments, evaluation, and interface design when putting AI agents into practice.

Starting from the trend of platforms absorbing basic RAG pipelines, this article examines the high-premium skills AI application engineers should prioritize in 2026: evaluation and observability, data governance and access control, and deep engineering capabilities for agentic workflows.

A systematic tour of the entire LLM training pipeline—from data pipelines, scaling laws, system constraints, synthetic data, distillation, post-training, and evaluation systems to agent training—and how these mechanisms affect model selection, evaluation, and harness design for AI application engineers.

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.