Prompt Engineering

Prompt engineering is the practice of crafting inputs to AI models to elicit better outputs. It's part art, part science—combining an understanding of how language models work with clear communication skills.

A prompt is any text you send to an AI model. Prompt engineering is the discipline of optimizing that text to get:

• More accurate responses
• More relevant information
• Responses in a specific format
• Consistent behavior across similar queries

While anyone can write a prompt, prompt engineering involves systematic techniques backed by research and experimentation.

The same AI model can give vastly different results depending on how you ask. Consider these two prompts:

❌ Weak prompt: "Write about dogs"

✅ Engineered prompt: "Write a 200-word guide for first-time dog owners covering the three most important things to know in the first week. Use a friendly, reassuring tone."

The second prompt specifies length, audience, structure, and tone—leading to a far more useful response.

Prompt engineering matters because:

It's cost-effective: Better prompts mean fewer retries and less wasted compute.

It unlocks capabilities: Many AI capabilities only emerge with the right prompting technique (like chain-of-thought reasoning).

It's accessible: Unlike fine-tuning, anyone can improve results through better prompts without technical infrastructure.

It's iterative: You can test and refine prompts quickly to optimize for your specific use case.

1. Be specific and explicit Don't assume the AI knows what you want. State your requirements clearly:

• Desired length
• Format (bullet points, paragraphs, JSON)
• Tone (formal, casual, technical)
• Audience (beginners, experts, children)

2. Use system prompts System prompts set the AI's persona and behavior. They're processed before user messages and establish context that persists throughout a conversation.

System: You are a helpful customer service agent for a software company.
Be concise, professional, and always offer to escalate complex issues to human support.

3. Provide examples (few-shot learning) Show the AI what you want by including examples:

Convert these product descriptions to bullet points:

Input: "Our coffee maker brews 12 cups in 10 minutes with programmable start times"
Output:
• 12-cup capacity
• 10-minute brew time
• Programmable start

Input: "Wireless earbuds with 8-hour battery and noise cancellation"
Output:

4. Chain-of-thought prompting Ask the AI to show its reasoning step by step. This dramatically improves accuracy on complex tasks:

Solve this problem step by step:
If a train travels 120 miles in 2 hours, then stops for 30 minutes,
then travels another 90 miles in 1.5 hours, what was its average speed
for the entire journey including the stop?

5. Use delimiters Clearly separate different parts of your prompt:

Summarize the following text:
"""
[Your text here]
"""

Role prompting Assign the AI a specific role to tap into relevant knowledge patterns: "You are a senior software architect reviewing code for security vulnerabilities..."

Structured output Request specific formats for programmatic processing: "Respond with valid JSON matching this schema: {name: string, score: number, reasons: string[]}"

Self-consistency Generate multiple responses and select the most common answer. Useful for reasoning tasks where the model might make errors.

Tree of thoughts Have the AI explore multiple reasoning paths, evaluate each, and select the best one. More thorough than simple chain-of-thought.

Prompt chaining Break complex tasks into steps, using the output of one prompt as input to the next. Each step can be optimized independently.

Constitutional AI prompting Include principles the AI should follow: "Always cite sources. Never make claims you can't verify. If unsure, say so."

Being too vague ❌ "Make it better" ✅ "Improve clarity by using simpler words and shorter sentences"

Overloading with instructions ❌ Cramming 20 requirements into one prompt ✅ Prioritize the most important requirements; use prompt chaining for complex tasks

Ignoring the model's limitations ❌ Asking for real-time data or recent events past the training cutoff ✅ Acknowledge limitations and provide necessary context

Not iterating ❌ Using your first prompt attempt in production ✅ Test multiple variations, measure results, refine systematically

Forgetting context limits ❌ Pasting an entire codebase into the prompt ✅ Include only relevant context; summarize when necessary

Create test cases Build a set of inputs with expected outputs. Run your prompt against all test cases whenever you make changes.

Use evaluation metrics

• Accuracy: Does the response match expected output?
• Relevance: Is the response on-topic?
• Completeness: Are all required elements present?
• Format compliance: Does it match the requested structure?

A/B test in production Run different prompt versions with real users and measure outcomes (click rates, task completion, user satisfaction).

Track prompt versions Use version control for prompts just like code. Document what changed and why.

Monitor for drift Model updates can change how prompts perform. Regularly re-evaluate your prompts against your test suite.