Prompt engineering is not magic—it's a systematic approach to getting better results from LLMs. The difference between a vague prompt and a structured one can be 10x improvement in output quality.
The Prompt Engineering Framework
Every good prompt has three components:
- Context: What should the model know?
- Instruction: What should it do?
- Constraint: What should it NOT do?
Structured Prompting Techniques
Few-shot prompting: Show 2-3 examples before asking your question. Models learn from patterns.
Chain-of-thought: Ask the model to "think step by step." This dramatically improves reasoning tasks.
Role-based prompting: "You are a senior engineer reviewing code..." sets the right tone and expertise level.
Common Mistakes
- ❌ Vague instructions ("make it better")
- ❌ Too much context (overwhelming the model)
- ❌ Not specifying output format
- ❌ Asking for contradictory things
Practical Example
Bad: "Explain RAG systems"
Good: "Explain RAG systems in 3 paragraphs for a junior engineer who knows Python but not retrieval systems. Focus on: what problem it solves, how it works, practical implementation steps."
Key Insight: Spend 5 minutes crafting the prompt. It will save you 30 minutes of iteration.