Foundations: Mastering Prompt Engineering

Prompt engineering is the art and science of designing inputs to guide AI toward accurate, relevant, and useful outputs. These ten principles are evidence-based.

Download Prompt Templates

These templates are designed to be copied and pasted directly into a large language model. They provide a structure for common academic tasks.

The 10 Evidence-Based Principles of Effective Prompting

Master these core techniques to dramatically improve your AI interactions. Each principle is backed by research and practical testing.

1. Clarity & Specificity

Tell the LLM exactly what you want, leaving no room for interpretation. LLMs are extraordinarily creative, so vague prompts can lead to varied, inconsistent outputs.

Instead of: "Produce a report based on this data"
Use: "List our five most popular products and write a one-paragraph description of each"

2. Context Provision

Furnish all necessary background information. Context helps the LLM narrow its vast knowledge to your specific needs and avoid generic outputs.

Example: "I am a college senior with a 3.5 GPA and I need an essay outline on the French Revolution's impact"

3. Role/Persona Assignment

Assigning a specific role directly influences tone, style, and domain expertise. This makes responses more focused and professional.

Example: "You are a patent lawyer. Explain the legal process for patenting an invention in simple terms"

4. Output Format Definition

Clearly specify the desired structure for machine-readable outputs like JSON, XML, or human-readable formats like lists and tables.

Example: "Return results in JSON: {'key': 'value'}" or "Provide a concise summary in bulleted list format"

5. Examples (Few-Shot Prompting)

Including examples can lead to massive improvements in accuracy. Even a single example can significantly guide the model to desired output structure and style.

Example: "Here's an example of a one-paragraph description for another product..." then provide your request

6. Iterative Refinement

Prompt engineering is rarely "one-and-done." Continuously refining prompts based on LLM responses is essential for improving quality.

Example: Start broad, then refine: "Based on the outline provided, expand on the target audience section..."

7. Conciseness/Information Density

LLM performance can decrease with prompt length. Improve "information density" by shrinking information into fewer words.

Instead of: "The overarching aim of this exceptionally well-structured..."
Use: "Produce high quality, readable, clear content"

8. Chain of Thought (CoT) Prompting

For complex problems, encourage step-by-step reasoning. This enhances reasoning capabilities and provides transparency into the model's logic.

Example: "Let's think step-by-step" or "Explain each step" for mathematical problems

9. Instructions over Constraints

Instruct the model what to do (positive instructions) rather than what not to do (constraints). Use clear binary "hard on/off rules."

Instead of: "Do not list video game names"
Use: "Only discuss the console, company, year, and total sales"

10. Testing & Data-Driven Approach

Test prompts empirically using a "Monte Carlo approach"—generate multiple outputs and evaluate quality for statistical reliability.

Example: Use a spreadsheet to track "Prompt," "Output," and "Good Enough" ratings across 10-20 attempts
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