Prompt Engineering Fundamentals

What is Prompt Engineering? Prompt engineering is the practice of crafting effective inputs to large language models to guide them toward desired outputs. It is both an art and a science that significantly impacts LLM performance.

Core Prompting Techniques

Zero-Shot Prompting Directly state what you want without examples:

Summarize the following article in 3 bullet points:
[article text]

Few-Shot Prompting Provide examples to guide the output format:

Translate English to French:
- Hello → Bonjour
- Goodbye → Au revoir
- Thank you → Merci
- How are you? →

Chain-of-Thought (CoT) Encourage step-by-step reasoning:

Solve this math problem step by step:
If a train travels 120 miles in 2 hours, what is its average speed?

ReAct (Reasoning + Acting) Combine reasoning with tool use:

Question: What is the population of Tokyo?
Thought: I need to search for current Tokyo population data.
Action: search["Tokyo population 2024"]
Observation: Tokyo metropolitan area has 37.4 million people.
Answer: The population of Tokyo metropolitan area is approximately 37.4 million.

Prompt Structure Best Practices 1. Be specific: "Write a 300-word professional email" not "Write an email" 2. Use delimiters: XML tags or markdown to separate sections 3. Specify format: JSON, bullet points, or structured output 4. Set persona: "You are an expert software architect..." 5. Include examples: Show desired input-output pairs

Common Mistakes

• Vague instructions leading to inconsistent outputs
• Not specifying output format
• Missing context or constraints
• Over-complicated prompts that confuse the model