Essay · Context Systems
Context Engineering Is the New Prompt Engineering
Stop optimizing prompts. Start engineering the context your models live in.
Prompt engineering rewarded cleverness. Context engineering rewards systems thinking. Instead of spending an afternoon wordsmithing one perfect prompt, design structured context that compounds with every use: canonical instructions, reusable exemplars, lightweight memory, and predictable interfaces into your work.
← Back to essaysWhy prompts plateau
- Prompts solve the question in front of you; tomorrow you start over.
- No institutional memory: lessons stay trapped in chat logs.
- Optimization efforts overfit to a single model release.
Context engineering flips the mindset. Treat every conversation as an opportunity to capture structure you can reuse: templates, rubrics, decision trees, domain glossaries, and references to real work.
Principles of context engineering
- Explicit structure beats cleverness. Write down the framing, constraints, and objectives you want the model to honor.
- Context should travel. Store it alongside your code or knowledge base so every builder can load it.
- Design for compounding. Keep snippets modular so you can stack them (persona + process + examples) as needed.
- Measure drift. When the model stops behaving, inspect the context stack first, not the prompt phrasing.
A simple context architecture
| Layer | Purpose | Artifacts |
|---|---|---|
| Anchor | Non-negotiables and tone | System instructions, operating principles |
| Patterns | How you want work done | Checklists, templates, rubrics |
| Examples | Concrete proof | Past deliverables, annotated snippets |
| Memory | Continuity across sessions | Scoped transcripts, decision logs |
The compounding comes from capturing each layer as code or content you can version, review, and improve - just like any other artifact.
How to get started this week
- Audit one workflow you keep repeating with AI (code review, PRD, etc.).
- Write the anchor: 3-5 lines describing who you are, who you serve, and what “good” means.
- Add a pattern: bullet the phases or checklist you expect.
- Save two annotated examples that show success and failure.
- Store everything in source control or your knowledge repo so others can load it.
After two or three iterations you stop prompt-hunting and start curating context libraries. That's the leap from prompt engineer to context architect.
Questions to keep asking
- What context components did we reuse this week?
- Where are we still relying on clever prompts?
- Which decisions should now live in our anchor or pattern layers?
- How do we onboard new teammates into these context systems?
Reflection prompt
The best AI builders I work with aren't prompt magicians - they're context engineers. What context system are you building right now?