Advanced Context Engineering: Systems, Evaluation & Research

About this course

This course contains the use of artificial intelligence.

Advanced Context Engineering is the advanced course that follows Context Engineering, Part I. It follows one idea through 60 lessons in 12 modules, about 19 hours in total: the context an AI agent works with is data that you assemble, check and measure. You start from a small baseline and finish with a complete system that you test on a sealed test set and defend with evidence.

Along the way you prepare documents and PDFs you can trust, assemble task packets whose claims trace to a file and a line, and compare word search, embeddings, rank fusion and reranking on the same questions. You build chained searches and a small graph, manage long context with budgets, handoffs and caches, and build memory with sources, dates and SQLite. You then build a local MCP server, run long tasks that can stop and resume, measure whether several agents help, plant instructions in data, enforce permissions in code, and build evaluation suites that catch regressions and show which component actually helped. The last modules cover reading a research paper and testing its idea, and a final project: a staff assistant for a fictional warehouse handbook.

Every lesson follows the same pattern: a short refresh, slides that explain the new idea, a continuous demonstration in Codex CLI, a close reading of the results, and a recap. Almost every lesson ends with an exercise and a solution to compare against. Lessons come with starter files and saved results and, in the later lessons, offline checks and scripted stand-ins that make no model calls, so you can inspect what was recorded before spending anything. The demonstrations use Codex CLI 0.156.1 with GPT-6-Luna at medium reasoning, and all data is synthetic, drawn from fictional organizations.

It is written for graduates of Part I and for people who write code or are ready to learn as they go.

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