Topic
Context Engineering
9 engineering lessons and 2 supporting resources about Context Engineering.
About this topic
This track collects 9 lessons on Context Engineering, written to be read in order and revisited as reference.
It opens with “What Is Context? Context Windows & Tokens for LLMs.” Explain why an LLM cannot see your whole codebase, what actually fills the context window, and why context retrieval — not the model — is where modern AI systems win or lose.
You’ll learn
- What Is Context? Context Windows & Tokens for LLMs
- Context Retrieval: Lexical vs Semantic vs Hybrid
- RAG, Chunking & Codebase Context Explained
- LLM Memory, Context Compression & Failure Modes
- Context Engineering: Architecture & Production Design
Recommended starting lessons
View all lessons- 01What Is Context? Context Windows & Tokens for LLMs
Explain why an LLM cannot see your whole codebase, what actually fills the context window, and why context retrieval — not the model — is where modern AI systems win or lose.
8 min - 02Context Retrieval: Lexical vs Semantic vs Hybrid
Explain how a system decides what to send to the model, and compare lexical, semantic, hybrid search, ranking and re-ranking by purpose, advantages, disadvantages, and failure modes.
13 min - 03RAG, Chunking & Codebase Context Explained
Explain how Cursor and Claude Code understand large repos, why chunking exists and how it fails, and how RAG works from first principles — and why most RAG bugs are retrieval bugs.
12 min
What this topic connects to
Related topics to explore next.
Resources in this track
Curated references, cheat sheets, and checklists.
- Context System Architecture-Review Checklist
A ten-question architecture-review checklist for any AI context system — context strategy, retrieval, chunking, index freshness, and evaluation.
- Context Engineering Cheat Sheet
A one-page context engineering cheat sheet: retrieval, chunking, RAG, embeddings, vector indexes, memory, compression, prompt caching, ordering, evaluation, and security at a glance.