Context Engineering for Large Language Models: A Practical Guide
As large language models become central to more applications, context engineering has emerged as a critical skill. Here are the key principles I’ve learned from building production LLM systems.
Start with Clear Instructions
The foundation of good context engineering is clarity. Your prompt should explicitly state what you want the model to do, in what format, and with what constraints.
| Prompt Quality | Example | Issues |
|---|---|---|
| Poor | ”Analyze this data” | Vague, no format specified |
| Better | ”Analyze the following sales data and provide three key insights in bullet point format, focusing on trends over the last quarter.” | Specific task, format, and scope |
| Best | ”You are a senior data analyst. Analyze the Q4 sales data below and provide exactly 3 actionable insights in bullet points. Focus on revenue trends, customer segments, and seasonal patterns. Include specific numbers where relevant.” | Clear role, explicit constraints, detailed requirements |
Structure Your Context Hierarchy
Organize information by importance and relevance:
- System instructions - Core behavior and role
- Immediate context - Current task and constraints
- Reference material - Background information and examples
- User input - The specific request
Manage Token Budgets Strategically
Context windows are finite. Prioritize:
- Recent conversation history over distant context
- Task-specific examples over general knowledge
- Structured data over verbose explanations
Token Estimation Quick Reference
| Content Type | Approximate Tokens | Best Use |
|---|---|---|
| Simple instruction | 10-50 | Core system prompts |
| Code example | 100-500 | Few-shot demonstrations |
| Documentation chunk | 500-2000 | Reference material |
| Full conversation | 1000-8000 | Context maintenance |
Pro tip: Use tools like OpenAI’s tiktoken to accurately count tokens before hitting context limits.
Use Examples Effectively
Few-shot examples are powerful but expensive. Choose examples that:
- Cover edge cases relevant to your use case
- Demonstrate the exact output format you want
- Show reasoning patterns, not just correct answers
Example Template Structure
## Task: [Brief description]
**Input:**
[Your input data/question here]
**Expected Output:**
[Exact format you want]
**Reasoning:**
[Step-by-step thought process]This template ensures consistency and helps the model understand both the what and the how of your desired response.
Test and Iterate
Context engineering is empirical. What works for one model or task may fail for another. Build systematic evaluation processes and be prepared to refine your approach based on real-world performance.
Evaluation Framework
- Baseline Testing - Start with simple prompts
- A/B Comparison - Test variations systematically
- Edge Case Analysis - Identify failure modes
- Performance Metrics - Track accuracy, consistency, latency
💡 Remember: The best context engineering combines technical precision with deep understanding of your specific domain and use case.