Prompt Engineering in Generative AI – Zero-shot, Few-shot & Chain-of-Thought Prompts

Learn the fundamentals of prompt engineering for Generative AI. Understand zero-shot, few-shot, and chain-of-thought prompting techniques to generate accurate and context-aware outputs using AI models.

1. Introduction

Prompt engineering is the process of designing effective input prompts to guide generative AI models (like GPT, Claude, or LLaMA) to produce accurate, relevant, and creative outputs.

  1. Good prompts improve model performance.
  2. Helps in generating consistent text, translations, code, summaries, or reasoning.
  3. Core for working with Large Language Models (LLMs).

2. Types of Prompts

2.1 Zero-shot Prompts

  1. The model receives only instructions without any examples.
  2. Relies on the model’s pretrained knowledge to produce outputs.

Example (Text Summarization):

Prompt: “Summarize the following paragraph in one sentence:
‘Artificial Intelligence is rapidly transforming technology and society by enabling new applications in healthcare, education, and business.’”

Python Example using OpenAI GPT:


from openai import OpenAI
client = OpenAI(api_key="YOUR_API_KEY")

response = client.chat.completions.create(
model="gpt-4",
messages=[{"role":"user","content":"Summarize the following paragraph in one sentence: 'Artificial Intelligence is transforming technology and society.'"}]
)
print(response.choices[0].message.content)

Use Cases:

  1. Quick summarization
  2. Translation without examples
  3. General Q&A

Best Practices:

  1. Be specific in instructions
  2. Use concise language
  3. Avoid ambiguity

2.2 Few-shot Prompts

  1. Provide examples along with instructions.
  2. Helps the model learn the pattern from examples and generate consistent outputs.

Example (Translation):

Prompt:
“Translate the following English sentences to French:
Example 1: 'Hello' → 'Bonjour'
Example 2: 'Goodbye' → 'Au revoir'
Translate: 'Thank you' → ?”

Python Example using OpenAI GPT:


prompt = """
Translate the following English sentences to French:
Example 1: 'Hello' → 'Bonjour'
Example 2: 'Goodbye' → 'Au revoir'
Translate: 'Thank you' → ?
"""

response = client.chat.completions.create(
model="gpt-4",
messages=[{"role":"user","content":prompt}]
)
print(response.choices[0].message.content)

Use Cases:

  1. Structured outputs (tables, lists)
  2. Text style transfer
  3. Consistent language generation

Best Practices:

  1. Provide 2-5 clear examples
  2. Ensure examples cover edge cases
  3. Keep examples concise and consistent

2.3 Chain-of-Thought (CoT) Prompts

  1. Encourage the AI to reason step by step before providing the final answer.
  2. Improves accuracy in complex reasoning, logic, math, and multi-step tasks.

Example (Math Problem):

Prompt:
“Solve the problem: A train travels 60 km in 1 hour and then 90 km in 2 hours. What is the average speed? Explain step by step.”

Python Example using OpenAI GPT:


prompt = """
Solve the problem step by step: A train travels 60 km in 1 hour and then 90 km in 2 hours.
What is the average speed? Explain your reasoning step by step.
"""

response = client.chat.completions.create(
model="gpt-4",
messages=[{"role":"user","content":prompt}]
)
print(response.choices[0].message.content)

Use Cases:

  1. Math problem solving
  2. Logical reasoning
  3. Multi-step instructions or decision making

Best Practices:

  1. Ask explicitly for step-by-step reasoning
  2. Use for complex, multi-step tasks
  3. Combine with few-shot examples for even better accuracy

3. General Best Practices in Prompt Engineering

  1. Clarity: Make instructions clear and concise.
  2. Context: Provide sufficient context when needed.
  3. Consistency: Use few-shot examples to guide output format.
  4. Iteration: Test multiple prompts to find the best results.
  5. Control Creativity: Adjust AI parameters like temperature to balance creativity and correctness.

4. Outcome

After mastering prompt engineering, beginners will be able to:

  1. Craft effective zero-shot, few-shot, and chain-of-thought prompts.
  2. Generate accurate, context-aware, and structured outputs from AI models.
  3. Optimize AI prompts for text generation, reasoning, translation, and creative tasks.
  4. Lay the foundation for practical Generative AI applications.