Context Engineering: Feeding AI the Right Information
The skill that replaced prompt engineering: RAG without the jargon, knowledge bases that answer well, and managing context over long work.
A mediocre prompt with the right document attached beats a perfect prompt about a document the model cannot see. That asymmetry is why many practitioners now call context engineering the most important skill in applied AI. This course teaches you to control what the model can see: the four context failures and their distinct signatures, retrieval-augmented generation explained without jargon (you are already using it), how to build a knowledge base that retrieves well instead of confidently citing the wrong chunk, where to put standing context so you stop re-pasting it, and how to stop long work from degrading. You will leave curating a workspace rather than chatting.
What you'll learn
- Diagnose missing, excessive, stale and wrong-format context by signature
- Explain and use RAG without needing any code
- Curate and structure a knowledge base that retrieves the right chunks
- Put standing context in projects and custom instructions properly
- Use checkpoint summaries to keep long work from degrading
- Treat context as a workspace you curate, not a conversation you have
Course content
- 1. Why context beats prompting (15 min)
- 2. The four context failures and how to spot them (15 min)
- 3. RAG without the jargon (15 min)
- 4. Building a knowledge base that answers well (15 min)
- 5. Standing context: projects, instructions, and briefs (15 min)
- 6. Managing context over long work (15 min)
Related courses
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- Prompting That Works: The Universal Framework — One reusable five-part brief that gets usable output from any model, plus the examples, reasoning checks and iteration habits that make it stick.
- Choosing Your AI Stack in 2026 — ChatGPT, Claude, Gemini, Copilot and open-weight models: how to choose on data handling, connections and fit rather than leaderboards.