Prompt Engineering Fundamentals

Prompt engineering is the art of communicating effectively with LLMs. Good prompts lead to better, more consistent outputs.

Core Principles

1. Be Specific

Vague prompts get vague responses. Specify exactly what you want.

Bad:  "Write about dogs"
Good: "Write a 200-word informative paragraph about Golden Retrievers,
       covering their temperament, exercise needs, and suitability
       for families with children."

2. Provide Context

Give the model relevant background information:

prompt = """
You are an expert Python developer with 10 years of experience.

Given this function:
{code}

Identify potential bugs and suggest improvements.
Focus on: error handling, edge cases, and performance.
"""

3. Use Examples (Few-Shot)

Show the model what you want through examples:

Convert these sentences to formal business English:

Input: "Hey, can you send that report?"
Output: "Could you please forward the quarterly report at your earliest convenience?"

Input: "The meeting's pushed back"
Output: "Please note that the meeting has been rescheduled to a later time."

Input: "Got it, thanks!"
Output:

Advanced Techniques

Chain of Thought

Ask the model to reason step-by-step:

Solve this problem step by step:
[Problem description]

Think through each step before giving your final answer.

Role Assignment

Define a specific persona:

You are a senior security engineer reviewing code for vulnerabilities.
Analyze the following code and identify any security issues.

Output Formatting

Specify the exact format you need:

Respond in JSON format with the following structure:
{
  "sentiment": "positive" | "negative" | "neutral",
  "confidence": 0.0 to 1.0,
  "key_phrases": ["phrase1", "phrase2"]
}

Common Pitfalls

1. Overloading - Don't ask for too much at once
2. Ambiguity - Eliminate room for misinterpretation
3. No constraints - Set boundaries on length, format, scope

Conclusion

Prompt engineering is iterative. Test, measure, and refine your prompts for consistent results.