What is context engineering?
Context engineering is the practice of designing everything an AI model sees before it starts working. Anthropic, the maker of Claude, defines it as curating and maintaining the optimal set of information a model gets during a task, in its guide toeffective context engineering for AI agents. The industry arrived at the same lesson we teach: the background beats the phrasing.
In practice, context engineering covers:
- Context files: reusable, plain-text briefs for recurring tasks.
- Reference systems: scattered files and notes turned into retrievable knowledge.
- Shared team context: one source of truth any AI tool can draw from.
- Memory: decisions recorded once instead of re-explained every session.
How is context engineering different from prompt engineering?
Prompt wording matters, but it cannot supply missing facts or decisions. Start with the goal, relevant examples, constraints, and source material. That is the practical starting point in Prompting Is Not the Product.
| Prompt engineering | Context engineering | |
|---|---|---|
| Focus | How you phrase one instruction | Everything the model sees before it works |
| Scope | One prompt at a time | Goals, examples, constraints, sources, memory |
| Durability | Fades when tools change | Transfers across every AI tool |
| Failure mode | Endless rewording | Fix the file once, benefit every run |
What is the context stack?
The context stack is a six-layer diagnostic. When output disappoints, find the empty layer instead of playing prompt roulette. The full walkthrough lives inContext Comes Before Prompting.
| Layer | Question it answers |
|---|---|
| Goal | What is this output for? |
| Audience | Who reads or uses it? |
| Examples | What does good look like? |
| Constraints | What are the rules? |
| Source material | What facts should it use? |
| Memory | What is already decided? |
How does context engineering work for a team?
Start with the task and the information it needs. Keep authoritative records distinct from working copies, define owners, and give each role the access it needs. Individuals can begin with a personal AI brain. Teams can then build a shared reference structure using the approach in Retrieval for Non-Technical Teams.
A scoped engagement can include a folder and reference structure, reusable context files, workflow documentation, and review responsibilities. We test these on a real task before expanding or automating the process.
Scope, access requirements, deliverables, and review responsibilities are agreed before implementation.
How do you start with context engineering?
- Pick one recurring task. One, not ten.
- Write a plain-text context file: goal, audience, two good examples, constraints, sources.
- Paste or attach it at the start of every session.
- When a draft misses, fix the file, not just the draft.
- Grow the folder of files into a reference system, then share it with your team.
For the daily filtering habit that keeps your context current, seeThe Signal Stack.
