Prompt Engineering

Learn how to write effective prompts that get the best results from multi-model queries.

Why Prompts Matter More with Multi-Model

When querying multiple AI models, your prompt is interpreted by different systems with different training data and response patterns. A well-crafted prompt ensures consistent, high-quality responses across all models, leading to better synthesis and consensus.

A prompt that works well with GPT-4 might confuse Claude, and vice versa. The techniques below help you write prompts that work consistently across all models.

Core Principles

1. Be Explicit About What You Want

Don't assume the AI knows what format or depth you need.

Instead of:

"Tell me about React hooks"

Write:

"Explain React hooks with a focus on useState and useEffect. Include a practical code example for each, and explain common mistakes beginners make."

2. Specify the Output Format

When you need structured output, describe the format explicitly.

Instead of:

"Compare Python and JavaScript"

Write:

"Compare Python and JavaScript in a table format with columns for: Typing System, Performance, Use Cases, and Learning Curve. Add a brief summary after the table."

3. Provide Context

Give background information that helps all models understand your situation.

Instead of:

"Should I use MongoDB?"

Write:

"I'm building a social media app with 10K expected users. Data includes user profiles, posts, and comments with complex relationships. Should I use MongoDB or PostgreSQL? Consider scalability, query patterns, and development speed."

4. Define Constraints and Requirements

Be clear about any limitations or requirements.

Instead of:

"Write a sorting function"

Write:

"Write a sorting function in TypeScript that sorts an array of objects by a specified key. It should handle null values, be type-safe, and work with both string and number keys. Include JSDoc comments."

Effective Prompt Templates

For Analysis Questions

Analyze [TOPIC] considering the following aspects:
1. [ASPECT 1]
2. [ASPECT 2]
3. [ASPECT 3]

Context: [RELEVANT BACKGROUND]

Please provide:
- A summary of key findings
- Pros and cons
- Your recommendation with reasoning

For Technical Decisions

I need to decide between [OPTION A] and [OPTION B] for [USE CASE].

Current situation:
- [CONSTRAINT 1]
- [CONSTRAINT 2]
- [REQUIREMENT 1]

Please compare both options considering:
1. Performance implications
2. Development complexity
3. Long-term maintainability
4. Cost considerations

Conclude with a clear recommendation.

For Code Review

Review the following code for:
1. Bugs or potential issues
2. Performance optimizations
3. Security vulnerabilities
4. Code style and best practices

Code:
[YOUR CODE HERE]

Provide specific suggestions with code examples where applicable.

For Explanation/Teaching

Explain [CONCEPT] to someone with [EXPERIENCE LEVEL] experience.

Include:
- A simple analogy
- The core concept in 2-3 sentences
- A practical example
- Common misconceptions to avoid
- Next steps for learning more

Multi-Model Specific Tips

Avoid model-specific terminology

Don't say "use your system prompt" or reference capabilities specific to one model.

Use standard formats

Markdown, JSON, and numbered lists are universally understood by all models.

Be unambiguous

Avoid idioms, slang, or cultural references that might be interpreted differently.

Request structured output

Structured responses are easier to synthesize across models.

Include evaluation criteria

When asking for recommendations, specify what factors matter most.

Common Mistakes to Avoid

Too Vague

"Make my code better"

Better: "Review this code for performance issues and suggest specific optimizations with examples."

Missing Context

"Which framework should I use?"

Better: "Which React state management library (Redux, Zustand, or Jotai) is best for a medium-sized e-commerce app with complex cart logic?"

No Success Criteria

"Is this a good approach?"

Better: "Evaluate this approach based on scalability, maintainability, and team learning curve. Provide a score for each."

Overloaded Questions

"Explain Docker, Kubernetes, and microservices and when to use each and best practices for all."

Better: Break into separate, focused questions for each topic.