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Context Engineering: The Real Skill Behind Good Prompts
Design context, not just prompts — what to include, what to leave out, budgeting tokens.
What You Will Learn
- Design context deliberately.
- Budget tokens per request.
- Select what to include vs exclude.
- Build a context-selection heuristic.
- Recognize context bloat.
Why This Matters
Beginners write prompts; intermediates engineer context. The same prompt with curated context outperforms a vague prompt with all-the-context. This lesson shifts you from prompt-thinking to context-thinking.
Concept Explained
Context engineering is the practice of deliberately selecting what the model sees: which files, what history, what instructions, what constraints. More context isn't better; the right context is better.
How It Works
For each request, ask: what does the model need to know to answer well? Include that. Exclude everything else. Budget tokens: system instructions (~200), your prompt (~500), attached files (~2000 each), chat history (~500 per turn). Aim to leave 50% of the window for the response.
Step-by-Step Tutorial
1. Identify the question
What are you asking? What's the minimum the model needs to answer well?2. Select files
Which files contain the answer or relevant context? Attach only those.3. Trim history
Does the model need full chat history? If not, start a new thread.4. Add constraints
What conventions must the model follow? Add as instructions, not history.5. Budget check
Estimate token count. If over 60% of window, trim more.Real-World Example
A developer wanted Copilot to refactor a function across 5 files. First attempt: attached all 5 files + full chat history. Copilot produced a generic refactor that didn't match the project's patterns. Second attempt: attached only the 2 files with the function and its callers, plus a one-line constraint ('use our existing service pattern'). Copilot produced a clean, idiomatic refactor.
Example Prompts / Commands / Code
Question Include Exclude
---------------------------------------------------------------
Refactor a function The function file Unrelated files
Direct callers Test files (unless asked)
Convention notes Chat history
Current selection Other open tabs
Debug an error Error message Unrelated code
Stack trace Old chat history
@terminal Other projects
Write a feature Target file Old features
Type definitions Unrelated modules
Convention notes README (usually)
Aim for:
System + prompt: ~10% of window
Attached files: ~20–40%
Chat history: ~10–20% (or 0 for new thread)
Response space: ~30–50%
If over 60% on input, trim. If under 20%, you may be under-informing.
Common Mistakes
- Attaching every file 'just in case' — dilutes signal.
- Keeping long chat history for unrelated tasks.
- Forgetting to budget for the response — Copilot truncates output.
- Not trimming when over budget — quality drops silently.
Best Practices
- Select files deliberately; less is more.
- Start new threads per task to reset history.
- Add conventions as instructions, not history.
- Budget ~30–50% of window for the response.
- Re-evaluate context when output quality drops.
Troubleshooting
| Problem | How to Fix |
|---|---|
| Output is generic | Add more specific context — attach the relevant file, add a constraint. |
| Output is too short | You may be over budget. Trim history or attachments. |
Practical Exercise
Your Turn
Take a recent Copilot request. List what you included. Apply the selection heuristic. Identify what you'd add, remove, or trim. Re-run with the optimized context and compare.
Professional Challenge
Build a 1-page 'context budget worksheet' for your team. Lists common task types, recommended inclusions, and token estimates. Share in your team's wiki.
Key Takeaways
- Context engineering > prompt engineering.
- Select deliberately; less is more.
- Budget 30–50% of window for response.
- Start new threads per task.
- Add conventions as instructions, not history.
Frequently Asked Questions
How much context is too much?
Does @workspace count against budget?
Further Reading
Official References
SEO Metadata
SEO title: Context Engineering: The Real Skill Behind Good Prompts
Meta description: Design context, not just prompts — what to include, what to leave out, budgeting tokens.
Primary keyword: context engineering
Secondary keywords: context engineering: the real skill behind good prompts
Search intent: Informational
URL slug: /context-engineering-real-skill-copilot
Categories: AI Tools, GitHub Copilot
Tags: GitHub Copilot, Intermediate, Context Engineering, Prompting, IMCSEIAN, Tutorial, IMCSEIAN
Featured image concept: IMCSEIAN lesson card for Context Engineering: The Real Skill Behind Good Prompts
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