Prompt Engineering Best Practices

Master the art of crafting effective prompts

By Synqly Team•Updated December 2025

Prompt engineering is the craft of making model behavior reliable. The goal isn’t “a good answer once”—it’s consistent, testable output across a wide range of inputs. In this guide you’ll learn: • How to structure prompts for clarity • When to use examples (few-shot) • How to reduce hallucinations with constraints • How to enforce output formats (JSON) • How to iterate safely with evals

Core Principles

Effective prompts follow these principles: • Be specific and clear about your intent • Provide context and examples • Structure complex requests step-by-step • Define output format explicitly • Set constraints and boundaries • Use delimiters for clarity

Prompt Structure

A well-structured prompt includes:

TypeScript

const prompt = `
# Task
[Clearly state what you want the model to do]

# Context
[Provide relevant background information]

# Instructions
1. [Step one]
2. [Step two]
3. [Step three]

# Examples
Input: [Example input]
Output: [Desired output]

# Constraints
- [Constraint 1]
- [Constraint 2]

# Output Format
[Specify exactly how you want the response formatted]
`;

Few-Shot Learning

Provide examples to guide model behavior:

TypeScript

const messages = [
  {
    role: 'system',
    content: 'You are a sentiment analyzer. Classify text as positive, negative, or neutral.'
  },
  {
    role: 'user',
    content: 'The product exceeded my expectations!'
  },
  {
    role: 'assistant',
    content: 'Sentiment: Positive\nConfidence: 95%'
  },
  {
    role: 'user',
    content: 'Terrible experience, would not recommend.'
  },
  {
    role: 'assistant',
    content: 'Sentiment: Negative\nConfidence: 98%'
  },
  {
    role: 'user',
    content: userInput // New input to classify
  }
];

Chain-of-Thought Prompting

Encourage step-by-step reasoning for complex tasks:

Pro Tip

Chain-of-thought prompting significantly improves accuracy on mathematical and logical reasoning tasks.

TypeScript

const prompt = `
Solve this problem step by step:

Problem: A store has 150 items. On Monday, 23% were sold. 
On Tuesday, 15 more items were sold. How many items remain?

Think through this carefully:
1. Calculate Monday's sales
2. Calculate remaining after Monday
3. Subtract Tuesday's sales
4. Provide final answer

Show your work for each step.
`;

Structured Output Formatting

Request responses in specific formats:

TypeScript

const prompt = `
Analyze this customer review and respond in JSON format:

Review: "${review}"

Required JSON structure:
{
  "sentiment": "positive" | "negative" | "neutral",
  "score": 0-100,
  "topics": ["topic1", "topic2"],
  "actionItems": ["action1", "action2"],
  "priority": "high" | "medium" | "low"
}

Return ONLY valid JSON, no additional text.
`;

Common Anti-Patterns to Avoid

Avoid these common mistakes: • Vague instructions like "make it better" • Overly complex prompts that confuse the model • Conflicting instructions • Assuming context the model doesn't have • Not specifying output constraints • Using ambiguous language • Forgetting to handle edge cases

Build a Prompt Library

Teams scale faster when prompts are reusable assets: • Store prompts with versioning • Document intended behavior and examples • Add tests/evals per prompt • Keep prompts small and composable • Track changes like code (PR reviews)

Testing and Iteration

Systematically improve your prompts: • Test with diverse inputs • Measure output quality consistently • A/B test prompt variations • Track token usage and costs • Document successful patterns • Create a prompt library for reuse

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