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.

LangChain’s breakdown of the agent harness connects context engineering, memory, MCP, and the agent loop into one coherent map.

Starting with the fundamental distinction between concurrency and parallelism, this guide systematically explains when to use Python’s GIL, threading, multiprocessing, asyncio, and concurrent.futures, how they work together, and how to choose among them.

Build a practical mental model for Python async programming in web backends and AI applications, from iteration protocols, generators, and coroutine objects through awaitables, Tasks, Futures, the event loop, structured concurrency, async iteration, and thread and process pools.

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.

A practical breakdown for AI engineers of what the M5’s changes over the M4—from CPU, cache, and memory bandwidth to Neural Accelerators—mean for local LLM and diffusion inference.

KV Cache is a key concept connecting Transformer theory with LLM engineering and deployment. Understanding it completes the path from how a model computes to how it runs.

Understand what LLM Chain-of-Thought (CoT) is and how prompt engineering can elicit Chain-of-Thought (CoT) from an LLM.