Context engineering for everyday work
Most quality wins come from better context, not better prompts. This module teaches the data, structure, and limits that should accompany any production-ish AI workflow.
What you will learn
- Decide which inputs should and should not be sent to a model.
- Build a context block for a recurring workflow.
- Recognize four common context failures.
How you’ll learn this module
Built around evidence-informed learning methods. Designed to support retrieval practice, feedback, and spaced review.
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Topics covered
Context typesExamples, glossary, history, policy.
BoundariesPublic, vendor-hosted, internal.
FreshnessStale context is worse than no context.
CompressionSummarizing inputs without losing intent.
Suggested learning sequence
01
Read
Context types and failure modes. ~10 min.
02
Audit
Pick a workflow; list what AI currently sees. ~15 min.
03
Rebuild
Design a cleaner context block. ~15 min.
Practical exercises
- For one recurring task, write the smallest sufficient context block.
- Identify one piece of context that should never leave your environment.
Practice with the AI Tutor
Run this module on a real workflow
Bring one piece of work into the tutor. It turns this module’s topics into risk flags, a practice mission, an experiment, and an evidence record.
Open the AI Tutor for this module