January 5, 2025•6 min read

Mastering Prompt Engineering

Prompt engineering is the art and science of crafting inputs that guide AI models to produce desired outputs. Let's explore advanced techniques.

The Fundamentals

Effective prompts share common characteristics:

Clear Instructions: Be specific about what you want
Context: Provide relevant background information
Examples: Show the desired format or style
Constraints: Define boundaries and limitations

Core Techniques

Zero-Shot Prompting

Ask directly without examples:

Classify the sentiment of this review as positive, negative, or neutral:

"The product exceeded my expectations in every way."

Zero-shot sentiment classification

Few-Shot Learning

Provide examples to guide the model:

Extract the company name and location from each sentence:

Input: "Apple is headquartered in Cupertino, California."
Output: Company: Apple, Location: Cupertino, California

Input: "Microsoft was founded in Redmond, Washington."
Output: Company: Microsoft, Location: Redmond, Washington

Input: "Tesla builds electric vehicles in Austin, Texas."
Output:

Few-shot information extraction

Chain-of-Thought

Encourage step-by-step reasoning:

Solve this problem step by step:

If a train travels 120 miles in 2 hours, and then 180 miles in 3 hours, what is its average speed?

Let's think through this:
1. First, calculate total distance
2. Then, calculate total time
3. Finally, divide distance by time

Chain-of-thought reasoning example

Advanced Patterns

ReAct (Reasoning + Acting)

Interleave thinking and actions:

const reactPrompt = `
Thought: I need to find information about AI safety
Action: search("AI safety research papers")
Observation: Found 10 recent papers on AI alignment
Thought: I should summarize the key findings
Action: summarize(papers)
`;

ReAct pattern for agents

Tree of Thoughts

Explore multiple reasoning paths:

Generate multiple possible approaches
Evaluate each approach
Select the most promising path
Continue exploration or backtrack if needed

Self-Consistency

Generate multiple responses and aggregate:

def self_consistent_answer(prompt, n=5):
    responses = [llm.generate(prompt) for _ in range(n)]
    return most_common(responses)

Self-consistency implementation

Prompt Templates

Create reusable patterns:

const templates = {
  classification: `
    Classify the following {item_type} into one of these categories: {categories}
    
    {item_type}: {input}
    Category:
  `,
  
  extraction: `
    Extract {fields} from the following text:
    
    Text: {input}
    
    Extracted information:
  `,
  
  summarization: `
    Summarize the following text in {length}:
    
    Text: {input}
    
    Summary:
  `
};

Reusable prompt templates

Optimization Tips

Principle	Bad Example	Good Example
Be Specific	Write about AI	Write a 500-word article explaining transformers for software engineers
Use Delimiters	Translate this text...	Use """ to clearly separate the text to translate
Iterate	Use first attempt	Test, identify issues, refine, repeat

Prompt optimization principles

The best prompts are discovered through experimentation and iteration.

Common Pitfalls

Avoid these mistakes:

Ambiguity: Unclear instructions lead to unpredictable results
Information Overload: Too much context can confuse

Implicit Assumptions: State requirements explicitly
No Constraints: Define output format and length

Measuring Success

Evaluate prompts systematically:

def evaluate_prompt(prompt, test_cases):
    results = []
    for input_data, expected_output in test_cases:
        actual_output = llm.generate(prompt.format(**input_data))
        score = calculate_similarity(actual_output, expected_output)
        results.append(score)
    
    return mean(results)

Systematic prompt evaluation

Prompt engineering is both an art and a science. Master the fundamentals, experiment with advanced techniques, and continuously refine your approach based on results.