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AI Core Concepts (Part 13): Prompt Engineering

Prompt Engineering is the practice of crafting effective inputs (prompts) to guide the outputs of generative models, especially large language models (LLMs) like GPT or Claude. It’s essential for improving quality, accuracy, and usefulness of responses.

1. What Is a Prompt?

A prompt is the input you give to a model. It can be:

A question: “What is the capital of France?”
An instruction: “Summarize this article in bullet points.”
A task context: “Translate the following to French: Hello, how are you?”

Prompt design determines how well the model performs a task.

2. Types of Prompting

🔹 Zero-shot Prompting

Ask a model to complete a task without examples.

Prompt: "Summarize this paragraph in two sentences:

Artificial Intelligence is revolutionizing various industries..."

🔹 One-shot Prompting

Provide one example of the desired behavior.

Prompt: "Correct the grammar in this sentence:
Input: 'He go to school every day.'
Output: 'He goes to school every day.'

Correct this:
Input: 'She walk home yesterday.'
Output:"

🔹 Few-shot Prompting

Provide a few examples for better results.

Prompt:
"Convert these instructions to JSON:

Instruction: 'Turn off the lights and close the blinds.'
JSON: { 'action1': 'turn off lights', 'action2': 'close blinds' }

Instruction: 'Lock the door and arm the alarm.'
JSON:"

3. Prompting Patterns

➤ Chain-of-Thought (CoT)

Ask the model to explain reasoning step-by-step.

Prompt: "If there are 3 apples and you take away 2, how many do you have?
Let's think step by step."

➤ Role Prompting

Assign a persona or expertise to the model.

Prompt: "You are a senior software engineer. Review this Python code for performance issues:"

➤ Format Prompting

Request specific formats like lists, JSON, tables, Markdown.

Prompt: "List three pros and cons of remote work. Format as a Markdown table."

4. Common Use Cases

Use Case	Prompt Example
Text generation	“Write a sci-fi story in 5 sentences”
Code generation	“Write a function to sort a list in Python”
Translation	“Translate ‘Good night’ to German”
Summarization	“Summarize this email for a manager”
Style Transfer	“Rewrite this in a more formal tone”

5. Tips for Better Prompt Engineering

Be clear and explicit
Specify output format
Use step-by-step thinking for complex tasks
Add examples for better consistency
Use system-level instructions (e.g., "You are an expert editor")

6. Advanced Techniques

Prompt Chaining: Output of one prompt feeds into another
Auto Prompting: Algorithms generate the optimal prompt
ReAct Prompting: Combine reasoning and action (popular in AI agents)
Retrieval-Augmented Generation (RAG): Add context to prompts from external sources

7. Tools for Prompt Engineering

📚 Further Resources

Awesome Prompt Engineering GitHub Repo

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