Prompt Engineering Best Practices
Techniques for crafting effective prompts: few-shot learning, chain-of-thought, and structured output.
What is Prompt Engineering?
Prompt engineering is the practice of designing inputs that guide language models to produce desired outputs. Well-crafted prompts improve accuracy, consistency, and reliability without model retraining.
Core Techniques
1. Clear Instructions
Be explicit about the task, format, and constraints. Example: "Summarize the following text in 3 bullet points, each under 20 words."
2. Few-Shot Learning
Provide 2-5 examples of the desired input-output pattern. This "teaches" the model by demonstration.
3. Chain-of-Thought (CoT)
Ask the model to explain its reasoning step-by-step. Improves accuracy on complex tasks like math or logic.
4. Structured Output
Request responses in JSON, YAML, or Markdown for easy parsing. Example: "Return as JSON with keys: summary, sentiment, entities."
Common Mistakes to Avoid
- Vague instructions ("Tell me about X" vs. "List 5 benefits of X for enterprise use")
- Overloading prompts with too many tasks at once
- Assuming the model has context it doesn't (provide all necessary info)
- Not testing variations (A/B test prompts for best performance)
Advanced Strategies
- Role Assignment: "You are a financial analyst. Analyze..."
- Negative Instructions: "Do not include personal opinions."
- Output Validation: Ask model to check its own work ("Review your answer for accuracy.")
- Temperature Tuning: Lower for consistency, higher for creativity.
Production Best Practices
In production, maintain prompt libraries with version control, automated testing, and performance monitoring. Track metrics like accuracy, latency, and token usage to optimize over time.
Need expert prompt engineering?
We design, test, and optimize prompts for production AI applications with measurable performance gains.
Learn About AI Services →