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 to effective 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 is the last five percent. The first ninety-five percent is what the model gets to see. That is the argument of Prompting Is Not the Product, and it is why rewording a prompt five times fixes nothing when the goal, examples, and source material are missing.
| 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 in Context 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?
The same way, at a larger scale: build the brain before the bots. A company's clients, offers, processes, and voice get organized so any AI tool can use them, hosted so the whole team works from one source of truth. Individuals start with a personal AI brain. Teams get most of the benefit of retrieval infrastructure without buying any, the argument of Retrieval for Non-Technical Teams.
The payoff is concrete. On one logistics operation moving 50,000+ shipments a month, a manual finance workflow went from 100 hours a month down to 15, an 85% reduction with $100K+ in first-year return. Nick's role was scoping the process and shaping the system architecture before any code was written. The automation worked because the context architecture came first.
Client identities are kept private. Figures are from a documented case study.
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, see The Signal Stack.
