Prompt Engineering

Prompt engineering is the art and science of crafting effective instructions for AI language models. It's the skill of communicating with AI systems to get the best possible outputs.

Think of it as programming in natural language. The prompt is your code, and the AI's response is your output. Small changes in wording can produce dramatically different results.

Why Prompt Engineering Matters

• Quality: Better prompts = better AI outputs
• Efficiency: Reduce iterations and save time/money
• Reliability: Get consistent, predictable results
• Capability: Unlock advanced AI behaviors

In 2026, prompt engineering is an essential skill for developers, product managers, content creators, and anyone working with AI.

Every great prompt follows a structure. Here's the proven formula:

The 6-Part Prompt Framework

1. Role/Persona: Who should the AI be?
2. Context: What's the background/situation?
3. Task: What specific action should it perform?
4. Format: How should the output look?
5. Constraints: What rules must it follow?
6. Examples: Show what good output looks like

Universal Base Prompt Template

Copy and customize this template for any task:

# Role
You are a [ROLE] with expertise in [DOMAIN].

# Context
[Describe the situation, background, or problem]

# Task
Your task is to [SPECIFIC ACTION].

# Requirements
- [Requirement 1]
- [Requirement 2]
- [Requirement 3]

# Output Format
Provide your response in the following format:
[DESCRIBE FORMAT - e.g., JSON, markdown, bullet points]

# Constraints
- [What to avoid]
- [Length limits]
- [Style guidelines]

# Example (Optional)
Input: [example input]
Output: [example output]

Output Example: Prompt Transformation

1. Zero-Shot Prompting

Direct instruction without examples. Works for simple, well-defined tasks.

Translate the following English text to French: "Hello, how are you?"

2. Few-Shot Prompting

Provide examples to show the AI the pattern you want.

Classify the sentiment of these reviews:
Review: "Amazing product, love it!" → Positive
Review: "Terrible, waste of money" → Negative
Review: "It's okay, nothing special" → Neutral
Review: "Best purchase I've ever made!" → 

3. Chain-of-Thought (CoT)

Ask the AI to think step-by-step. Dramatically improves reasoning tasks.

Solve this problem step by step:
A store has 50 apples. They sell 15 in the morning and receive 20 more.
Then they sell 30% of what they have. How many apples remain?

Let's think through this step by step...

4. Role Prompting

Assign a specific persona or expertise to the AI.

You are a senior software architect with 20 years of experience.
Review this code and identify potential scalability issues...

5. Structured Output

Specify the exact format you want.

Analyze this product and return your analysis in JSON format:
{
  "pros": ["..."],
  "cons": ["..."],
  "rating": 1-10,
  "recommendation": "..."
}

Self-Consistency

Generate multiple responses and pick the most common answer. Great for math and reasoning.

Tree of Thoughts (ToT)

Explore multiple reasoning paths and evaluate which is best. Like chain-of-thought but branching.

ReAct (Reasoning + Acting)

Combine reasoning traces with tool use. The AI thinks, then acts, then observes.

Question: What is the population of Israel's largest city?
Thought: I need to find the largest city in Israel first.
Action: Search[largest city Israel]
Observation: Tel Aviv is the largest city in Israel.
Thought: Now I need the population of Tel Aviv.
Action: Search[Tel Aviv population 2026]
Observation: Tel Aviv has approximately 460,000 residents.
Answer: Tel Aviv, Israel's largest city, has about 460,000 people.

Prompt Chaining

Break complex tasks into multiple prompts, using output from one as input to the next.

1. Prompt 1: Generate outline
2. Prompt 2: Expand each section
3. Prompt 3: Edit and polish

Constitutional AI Prompting

Add explicit rules and constraints the AI must follow.

Rules:
- Never make up information
- Always cite sources when available
- If uncertain, say so clearly
- Keep responses under 200 words

System prompts set the AI's behavior, personality, and constraints before the conversation begins. They're critical for production applications.

Anatomy of a Great System Prompt

1. Identity: Who/what is the AI?
2. Purpose: What is its goal?
3. Capabilities: What can it do?
4. Constraints: What should it NOT do?
5. Style: How should it communicate?
6. Format: What format for responses?

Example System Prompt

You are a helpful coding assistant specialized in Python and JavaScript.

Your purpose is to help developers write clean, efficient, and well-documented code.

Capabilities:
- Write and debug code
- Explain concepts clearly
- Suggest best practices
- Review code for issues

Constraints:
- Never execute code that could be harmful
- Don't provide code for malicious purposes
- Acknowledge when you're unsure

Style:
- Be concise and direct
- Use code examples liberally
- Explain your reasoning

Format:
- Use markdown for formatting
- Include code blocks with syntax highlighting
- Add comments in code for clarity

Common Mistakes

• Too vague ("be helpful")
• Too restrictive (limiting useful behaviors)
• Contradicting instructions
• No examples of desired behavior

Copy these templates and customize them for your needs. Each template is battle-tested and optimized for best results.

1. Code Review Template

# Role
You are a senior software engineer conducting a code review.

# Task
Review the following code for:
1. Bugs and potential errors
2. Security vulnerabilities
3. Performance issues
4. Code style and readability
5. Best practices violations

# Code to Review
```[language]
[PASTE YOUR CODE HERE]
```

# Output Format
For each issue found, provide:
- **Location**: Line number or function name
- **Severity**: Critical / High / Medium / Low
- **Issue**: What's wrong
- **Fix**: How to fix it with code example

If the code is good, say so and suggest any minor improvements.

2. Content Writing Template

# Role
You are an expert content writer specializing in [TOPIC/INDUSTRY].

# Task
Write a [CONTENT TYPE: blog post/article/guide] about [TOPIC].

# Target Audience
[Describe your audience: beginners, professionals, etc.]

# Requirements
- Length: [X] words
- Tone: [Professional/Casual/Educational]
- Include: [specific points to cover]
- SEO keywords: [list keywords]

# Structure
1. Hook/Introduction
2. Main points (3-5)
3. Examples or case studies
4. Actionable takeaways
5. Conclusion with CTA

# Constraints
- No fluff or filler content
- Use short paragraphs (2-3 sentences)
- Include subheadings every 200-300 words

3. Data Analysis Template

# Role
You are a data analyst with expertise in [DOMAIN].

# Context
I have the following dataset:
[DESCRIBE OR PASTE DATA]

# Task
Analyze this data and provide:
1. Key insights and patterns
2. Statistical summary
3. Anomalies or outliers
4. Recommendations based on findings

# Output Format
Structure your analysis as:
- **Executive Summary** (2-3 sentences)
- **Key Findings** (bullet points)
- **Detailed Analysis** (with numbers)
- **Recommendations** (actionable items)

# Constraints
- Support all claims with data
- Use percentages and comparisons
- Highlight the most important finding first

Output Example: Code Review in Action

ChatGPT / GPT-4

• Responds well to structured prompts
• Can follow complex multi-step instructions
• Benefits from explicit role assignment
• Supports function calling for structured outputs

Claude

• Excels at following nuanced instructions
• Strong reasoning capabilities
• Responds well to XML-style tags for structure
• Particularly good at code tasks
• Uses artifacts for code and documents

Gemini

• Strong multimodal capabilities (text + images)
• Good at following instructions in context
• Effective with grounded generation

Open Source (Llama, Mistral)

• More sensitive to prompt format
• May require more explicit instructions
• Follow model-specific chat templates
• Performance varies by task

Universal Best Practices

• Be specific and unambiguous
• Provide context and examples
• Specify output format explicitly
• Test and iterate